Computer-implemented methods and evaluation systems for evaluating at least one image data set of an imaging region of a patient, computer programs and electronically readable storage mediums

ABSTRACT

At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application hereby claims priority under 35 U.S.C. § 119 toEuropean patent application numbers EP 21171600.6 filed Apr. 30, 2021and EP 21194933.4 filed Sep. 3, 2021, the entire contents of each ofwhich are hereby incorporated herein by reference.

FIELD

Some example embodiments relate to computer-implemented methods forevaluating at least one image data set of an imaging region of apatient, wherein at least one evaluation information describing at leastone medical condition in an anatomical structure of the imaging regionis determined. Other example embodiments relate to evaluation systems,computer programs and electronically readable storage mediums.

BACKGROUND

Imaging techniques are nowadays often used for diagnosis, monitoring andaftercare in medicine. An image data set of an imaging region of apatient is acquired and is then evaluated regarding medical concepts, inparticular pathological and other anatomical and/or physiologicalanomalies and features. While, in particular in the past, evaluation wasoften done purely manually by so-called reading, in particular by aradiologist, with the rise of the number of imaging exams and differentspecialized imaging techniques, the workload and complexity of theevaluation tasks increase.

Hence, the development of evaluation algorithms working on a computingdevice to aid medical staff in reading medical image data sets, inparticular regarding diagnosis, is an active field of research. Suchevaluation algorithms use input data comprising at least one image dataset to generate output data describing evaluation results, in particularphysical features depicted in and/or derivable from the image data set.Recently, more evaluation algorithms based on artificial intelligencehave been proposed, for example comprising a neural network. Suchartificial intelligence evaluation algorithms are trained using trainingdata comprising image data sets with associated ground truths regardingthe output data, for example respective annotations made in a manualevaluation by a physician.

Evaluation algorithms based on artificial intelligence, in particulardeep learning techniques, are often perceived as a “black box”, suchthat the results are in many cases not explainable and simply have to beaccepted by the user. For example, if a certain disease scoresummarizing several aspects of a disease is provided, the factorsleading to a certain result can, in particular in the case of standarddeep learning, not be ascribed to certain image features of the imagedata set. Some disease scores proposed by organizations follow a complexscheme of their determination, which oftentimes involve theinterpretation of a reading physician. Such interpretations are learnedby deep learning approaches and respectively mimicked.

In an example, coronary artery disease (CAD) is caused by the build-upof plaques in the arteries supplying the muscles of the heart, that is,the arteries in the so-called coronary artery tree. These plaques leadto a reduction in blood flow, which in turn can lead to stable orunstable angina, myocardial infarction, or sudden cardiac death, inparticular in the case of a plaque rupture. In 2015, CAD affected 110million people and resulted in 8.9 million deaths, which makes it one ofthe most common causes of death globally.

The recently updated National Institute of Health Care Excellence (NICE)and European Society of Cardiology (ESC) guidelines recommend cardiaccomputed tomography (CT) for risk stratification and as first-line testto diagnose CAD in symptomatic patients with suspected CAD, in whomobstructive CAD cannot be excluded by clinical assessment alone.Furthermore, the assessment of the coronary artery calcium score withcomputed tomography is considered as a risk modifier in thecardiovascular risk assessment of asymptomatic subjects and recommendedas such by the American Heart Association (AHA) guideline on themanagement of blood cholesterol.

Hence, coronary computed tomography angiography (CCTA) scans and calciumscoring (CaSc) scans are widely used in the routine assessment ofpatients with low to intermediate pretest probability of coronary arterydisease. Due to the newly released guidelines, this trend will continue,and it is expected that the number of CCTA and CaSc examinations willcontinue to rise. The increase in imaging exams leads to increaseddemands for the reading of the image data sets acquired in these scans.Thus, solutions are required that increase efficiency during reading andreporting of such examinations and yet provide a comprehensibleassessment of the general disease grade of the patient aiding in thediagnosis and in particular in decisions regarding further treatment.

Until now, the diagnostic workup of CCTA and CaSc scans is typicallydone manually, either by directly assessing the axial slices of theimage data sets or by making use of dedicated interactive softwaresolutions. For CCTA evaluation, the reader typically evaluates the threemain coronary arteries and their side branches separately for anyvisually perceivable sign of disease. Signs of disease may be anarrowing of the lumen or a widening of the outer wall of the vessel.The vessel wall itself may include bright structures (calcifications),darker structures (soft plaques) and mixtures of both (mixed plaques).The severity of disease is typically evaluated by measuring thenarrowing of the vessel lumen by computing the stenosis gradequantitatively and qualitatively. Plaques may further be associated withhigh risk plaque features like napkin ring signs, positive remodeling,spotty calcification or low attenuation. The identified findings arethen described in free text form, or as structured information in thereport. To standardize reporting, various reporting schemes have beenproposed. Most commonly used is the 17-segment model of the AmericanHeart Association (AHA), which has been extended to 18 segments by theSociety of Cardiovascular Computed Tomography (SCCT). Finally, theextent of disease is summarized on patient level by a standardizedevaluation information like “one vessel disease”, “two vessel disease”,or “three vessel disease” or by using the recently released CAD-RADSreporting scheme, see R. C. Cury et al., “Coronary ArteryDisease—Reporting and Data System (CAD-RADS): An Expert ConsensusDocument of SCCT, ACR and NASCI: Endorsed by the ACC”, JACC Cardiovasc.Imaging 9 (2016), pages 1099-1113.

For CaSc scan evaluation, typically the total and the vessel-specific“Agatston Score” is reported, which is a measure of the amount ofcalcification present in the individual subtrees and the whole coronarytree, see C. H. McCollough et al., “Coronary artery calcium: amulti-institutional, multimanufacturer international standard forquantification at cardiac CT”, Radiology 243(2007), pages 527-538.

The evaluation of the image data sets of both scans is typicallyperformed using postprocessing software that supports the reading bypreparing suitable views fully automatically. For CCTA evaluation, themajor coronary arteries are identified automatically and displayed asCurved Planar Reformations (CPRs), which allow to evaluate the wholevessel course in a single view. For CaSc evaluation, the native CT imagedata set is being thresholded and calcification candidates arehighlighted. The user then clicks onto the candidates to confirm orreject them, and to assign them to the individual subtrees.

In any case, these procedures are user-driven, interactive, and timeconsuming. In clinical routine, users spend a considerable amount oftime for documenting obvious findings, leaving less time for the actualinterpretation of the more complicated conditions.

As already mentioned above, ongoing research focuses on evaluationalgorithms that relieve the user of this burden by using artificialintelligence (AI) to automate the detection and classification ofdiseases, including the exemplarily discussed coronary artery disease.Evaluation algorithms trained using machine learning may also be calledtrained evaluation functions. An overview of this field of research withfocus on cardiovascular imaging is given in an article by D. Dey et al.,“Artificial Intelligence in Cardiovascular Imaging: JACCState-of-the-Art Review”, J. Am. Coll. Cardiol. 73(2019), pages1317-1335.

SUMMARY

Regarding the resulting evaluation information, the user may not be ableto understand how the evaluation system arrived at its conclusions. Inparticular, the user cannot interact with the evaluation system suchthat assessment of certain features may be consistently modified.

At least one example embodiment provides a computer-implementedevaluation method and evaluation system that provides summarizingevaluation information in an automatic, yet understandable manner and inparticular allows consistent, interactive modification of theautomatically derived assessment.

This object is achieved by providing a computer-implemented method,evaluation system, computer program and electronically readable storagemedium according to the independent claims. Advantageous embodiments aredescribed by the dependent claims.

According to at least one example embodiment a computer-implementedmethod for evaluating at least one image data set of an imaging regionof a patient, wherein at least one evaluation information describing atleast one medical condition in an anatomical structure of the imagingregion is determined, wherein the method comprises segmenting andlabelling the anatomical structure using at least one first evaluationalgorithm to generate at least one first evaluation data set andentering the at least one first evaluation data set into an evaluationdatabase; determining at least one second evaluation data set describingat least one local medical feature in the anatomical structure using atleast one second evaluation algorithm and entering the at least onesecond evaluation data set into the evaluation database; determining theat least one evaluation information by applying inference rules of arule set, wherein each inference rule derives at least one resultevaluation data set from at least one input evaluation data sets andwherein the evaluation database is augmented by adding the resultevaluation data sets for each rule application linked to the respectiveinput evaluation data sets in the evaluation database; and outputting aninteractive presentation comprising information from at least a part ofthe evaluation data sets of the evaluation database, wherein theinitially displayed interactive presentation comprises at least one ofthe at least one evaluation information and, when a user interactioncommand related to at least one chosen result evaluation data set isreceived, updating the interactive presentation to include informationof at least one input evaluation data set from which the chosen resultevaluation data set is derived.

According to at least one example embodiment, the interactivepresentation comprises (i) at least one of image data from the imagedata set, (ii) visualization data derived from at least one of the atleast one image data set or from at least one evaluation data set, or(iii) at least one overview image.

According to at least one example embodiment, at least a part of theinformation from the evaluation data sets included in the interactivepresentation is presented by at least one of annotating presented imagedata, overlaying the presented image data, modifying the presented imagedata or visualization data.

According to at least one example embodiment, at least one evaluationdata set is modified based on received user input, wherein allevaluation data sets linked to the modified evaluation data sets in theevaluation database are at least one of (i) marked, (ii) all resultevaluation data sets derived using the modified evaluation data sets areupdated using the respective inference rules, (iii) at least a part ofat least one evaluation data set or sets from which a modifiedevaluation data set was derived is marked as less reliable, or (iv)excluded from the interactive presentation.

According to at least one example embodiment, the anatomical structureis hierarchically divided into segments in multiple hierarchy levels,wherein at least a part of the result data sets (i) is determined for adefined hierarchy level or (ii) comprises evaluation data relating to adefined segment.

According to at least one example embodiment, the rules set comprises atleast one of (i) inference rules for deriving lowest hierarchy levelresult evaluation data sets from at least one of the at least one secondevaluation data sets, respectively, or (ii) inference rules deriving ahigher hierarchy level result evaluation data set from at least onelower hierarchy level input evaluation data set.

According to at least one example embodiment, at least one of additionalpatient data or statistical data are received, wherein information fromat least one of the additional patient data or statistical data is usedby at least one inference rule.

According to at least one example embodiment, if a finalizing usercommand is received, a combination data set comprising at least one ofthe at least one image data set or at least a part of the evaluationdata of the evaluation data sets, is compiled and provided for storing.

According to at least one example embodiment, the method includesdeciding, in which, before applying any first or second evaluationalgorithm, the at least one image data set is analyzed regarding (i)suitability for at least one of the first evaluation algorithm or thesecond evaluation algorithm, (ii) to determine at least one of asuitable first evaluation algorithm or a suitable second evaluationalgorithm, or (iii) if multiple image data sets are received, associateimage data sets to sets of the at least one of the first evaluationalgorithm or the second evaluation algorithm.

According to at least one example embodiment, the anatomical structureis a coronary artery tree, wherein at least one coronary computedtomography angiography scan and at least one calcium scoring computedtomography scan are received as image data sets and the evaluationinformation comprises at least one atherosclerotic disease-related scoreand at least one calcium score for the patient.

According to at least one example embodiment, as a preprocessing step,the cardiac-gated coronary computed tomography angiography scan is splitinto several image data sets according to multiple phases of the heartcycle, wherein at least one of (i) the image data set of a predefinedheart phase is selected for evaluation or (ii) at least one of the imagedata set or a subset of the at least one image data set best meetingrequirements of at least one of the first evaluation algorithm or thesecond evaluation algorithm is forwarded to the respective at least oneof the first evaluation algorithm or the second evaluation algorithm.

According to at least one example embodiment, after the at least onefirst evaluation data set has been determined, which describes segmentsof the coronary artery tree, for at least one of at least one segment orat least one group of segments, the determination of meeting therequirements is performed on subsets only showing at least one of thesegment or group, respectively.

According to at least one example embodiment, to segment and label thecoronary artery tree as anatomical structure: centerlines of coronaryarteries are detected by at least one of the at least one firstevaluation algorithm, the coronary lumen surrounding the centerlines isdetected by at least one of the at least one first evaluation algorithm,and the detected coronary arteries are classified according to at leastone of a predefined classification scheme or a user-selectableclassification scheme of the coronary artery tree into segments forlabelling by at least one of the at least one first evaluationalgorithm, such that the at least one first evaluation data setdescribes, for each point in the coronary artery tree, to which segmentthe point belongs, the local course of the segment and the local shapeof the segment.

According to at least one example embodiment, the at least one secondevaluation algorithm detects and analyzes lesions in the coronary arterytree such that a second evaluation data set is generated for eachlesion, comprising at least one information chosen from the groupcomprising: a start position and end position of the lesion, a plaqueclass, a plaque vulnerability information derived from or describing thepresence of at least one vulnerability indicator, positive remodeling,spotty calcification, and napkin ring signs, or a stenting informationdescribing the presence of a stent from an earlier intervention.

According to at least one example embodiment, the inference rules deriveresult evaluation data from at least one of the first evaluation dataset or the second evaluation data set for three hierarchy levels, thethree hierarchy levels including a lesion level relating to singlelesions, a segment level relating to single segments of coronaryarteries, and a patient level relating to the whole coronary arterytree.

According to at least one example embodiment, the interactivepresentation includes at least one of a coronary unfolded view, aschematic view, at least one lesion-specific view, or a percentile chartrelating to at least one calcium score.

According to at least one example embodiment, an evaluation system forevaluating at least one image data set of an imaging region of a patientto determine at least one evaluation information describing at least onemedical condition in an anatomical structure of the imaging region,wherein the evaluation system comprises an image interface configured toreceive the at least one image data set; a storage device configured tostore an evaluation database and a rule set; at least one firstdetermination unit configured to segment and label the anatomicalstructure using at least one first evaluation algorithm to generate atleast one first evaluation data set and determine at least one secondevaluation data set describing at least one local medical feature in theanatomical structure using at least one second evaluation algorithm,wherein the at least one first determination unit is adapted to enterthe at least one first and the at least one second evaluation data setinto the evaluation database; a second determination unit configured todetermine the at least one evaluation information by applying inferencerules of the rule set, wherein each inference rule derives at least oneresult evaluation data set from at least one input evaluation data set,wherein the second determination unit is adapted to augment theevaluation database by adding the result evaluation data sets for eachrule application linked to the respective input evaluation data sets inthe evaluation database; and a user interface unit configured to outputan interactive presentation comprising information from at least a partof the evaluation data sets of the evaluation database, wherein theinitially displayed interactive presentation comprises at least one ofthe at least one evaluation information and the user interface isadapted to receive a user interaction command related to at least onechosen result evaluation data set and to update the interactivepresentation to include information of at least one input evaluationdata set from which the chosen result evaluation data set is derived.

According to at least one example embodiment, a computer program, whenexecuted by a computing device of an evaluation system, is configured tocause the evaluation system to perform a method according to an exampleembodiment.

According to at least one example embodiment, an electronically readablestorage medium having instructions, when executed by a computing deviceof an evaluation system, is configured to cause the evaluation system toperform a method according to an example embodiment.

According to at least one example embodiment, an evaluation system forevaluating at least one image data set of an imaging region of a patientto determine at least one evaluation information describing at least onemedical condition in an anatomical structure of the imaging region,wherein the evaluation system comprises a storage device configured tostore an evaluation database and a rule set; and at least one processorconfigured to execute computer-readable instructions to cause theevaluation system to, receive the at least one image data set, segmentand label the anatomical structure using at least one first evaluationalgorithm to generate at least one first evaluation data set anddetermine at least one second evaluation data set describing at leastone local medical feature in the anatomical structure using at least onesecond evaluation algorithm, wherein the at least one firstdetermination unit is adapted to enter the at least one first and the atleast one second evaluation data set into the evaluation database,determine the at least one evaluation information by applying inferencerules of the rule set, wherein each inference rule derives at least oneresult evaluation data set from at least one input evaluation data set,wherein the second determination unit is adapted to augment theevaluation database by adding the result evaluation data sets for eachrule application linked to the respective input evaluation data sets inthe evaluation database, and output an interactive presentationcomprising information from at least a part of the evaluation data setsof the evaluation database, wherein the initially displayed interactivepresentation comprises at least one of the at least one evaluationinformation and the user interface is adapted to receive a userinteraction command related to at least one chosen result evaluationdata set and to update the interactive presentation to includeinformation of at least one input evaluation data set from which thechosen result evaluation data set is derived.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and features of example embodiments will become apparentfrom the following detailed description considered in conjunction withthe accompanying drawings. The drawings, however, are only principlesketches designed solely for the purpose of illustration and do notlimit example embodiments. The drawings show:

FIG. 1 a general flow chart of a method according to at least oneexample embodiment,

FIG. 2 a principle drawing of an initial state of an interactivepresentation according to at least one example embodiment,

FIG. 3 a principle drawing of a second state of the interactivepresentation according to at least one example embodiment,

FIG. 4 a principle drawing of a third state of the interactivepresentation according to at least one example embodiment,

FIG. 5 a principle drawing of a fourth state of the interactivepresentation according to at least one example embodiment, and

FIG. 6 a schematical drawing of an evaluation system according to atleast one example embodiment.

DETAILED DESCRIPTION

According to at least one example embodiment, a computer-implementedmethod for evaluating at least one image data set of an imaging regionof a patient, wherein at least one evaluation information describing atleast one medical condition in an anatomical structure of the imagingregion is determined, comprises:

segmenting and labelling the anatomical structure using at least onefirst evaluation algorithm to generate at least one first evaluationdata set and entering the at least one first evaluation data set into anevaluation database,

determining at least one second evaluation data set describing at leastone local medical feature in the anatomical structure using at least onesecond evaluation algorithm and entering the at least one secondevaluation data set into the evaluation database,

determining the at least one evaluation information by applyinginference rules of a rule set, wherein each inference rule derives atleast one result evaluation data set from at least one, in particular atleast two, input evaluation data sets and wherein the evaluationdatabase is augmented by adding the result evaluation data sets for eachrule application linked to the respective input evaluation data sets inthe evaluation database, outputting an interactive presentationcomprising information from at least a part of the evaluation data setsof the evaluation database, wherein the initially displayed interactivepresentation comprises at least one of the at least one evaluationinformation and, when a user interaction command related to at least onechosen result evaluation data set is received, updating the interactivepresentation to include information of at least one input evaluationdata set from which the chosen result evaluation data set is derived.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the singular forms, signified e.g. by thearticles “a,” “an,” and “the,” are intended to include the plural formsas well and vice versa, unless the context clearly indicates otherwise.As used herein, the terms “and/or” and “at least one of” include any andall combinations of one or more of the associated listed items.

In preferred embodiments, at least one of the at least one first and/orsecond evaluation algorithms comprises a trained evaluation function, inparticular a deep neural network and/or trained by a deep learningtechnique.

In general, a trained function mimics cognitive functions that humansassociate with other human minds. In particular, by training based ontraining data the trained function is able to adapt to new circumstancesand to detect and extrapolate patterns.

In general, parameters of a trained function can be adapted by training.In particular, supervised training, semi-supervised training,unsupervised training, reinforcement learning and/or active learning canbe used. Furthermore, representation learning (an alternative term is“feature learning”) can be used. In particular, the parameters of thetrained functions can be adapted iteratively by several steps oftraining.

In particular, a trained function can comprise a neural network, asupport vector machine, a decision tree and/or a Bayesian network,and/or the trained function can be based on k-means clustering,Q-learning, genetic algorithms and/or association rules. In particular,a neural network can be a deep neural network, a convolutional neuralnetwork or a convolutional deep neural network. Furthermore, a neuralnetwork can be an adversarial network, a deep adversarial network and/ora generative adversarial network.

Example embodiments, conceptually, provide an “explainable artificialintelligence (AI)” system. A combination of modern, in particular deeplearning-enabled, fully automated image evaluation algorithms with theapproaches of a traditional rule based expert system, which encodesdomain specific inference rules and derives new knowledge from localmeasurements by employing an inference mechanism, is proposed.

A segmentation in at least one of the at least one image data set by atleast one first evaluation algorithm provides information about theanatomy and/or other features in the at least one image data set. Thisallows to semantically localize further evaluation results, inparticular if also labelling is provided. In preferred embodiments, deeplearning based approaches may be applied to derive geometricalinformation concerning the anatomical structure and/or to labelsubstructures, for example segments of a blood vessel tree, according toa labelling scheme. In embodiments, the relevant parts of the anatomyare extracted as geometrical models, which form first evaluation data inthe at least one first evaluation data set. These models may then beautomatically labeled, in particular using a predefined, preferredterminology, for example according to a medical or clinical standard.This facilitates that at any time the location of medical features, forexample findings, can be presented and documented using the propermedical terminology.

In a further example embodiment, at least one medical, in particularclinically relevant, feature, for example a pathology and/or lesionand/or finding, is detected on a localized level. Preferably, traineddeep learning second evaluation algorithms are used to detect themedical features in at least one of the at least one image data set. Foreach medical feature, for example, the location and further meta data,for example a characterization of a pathology, may be determined assecond evaluation data. Preferably, for each such finding, that is eachsuch medical feature, a second evaluation data set is determined andstored into the evaluation database. These second evaluation data sets,as they each refer to a single, localizable medical feature and form thebasis for all further evaluation, may also be called “atomic” evaluationdata sets.

In at least one example embodiment, these (usually multiple) second(“atomic”) evaluation data sets together with the at least one firstevaluation data sets stored in the evaluation database are seen as aknowledge base, from which further result evaluation data, which, ofcourse, comprises the evaluation information, is derived in a rule-baseddiagnostic workup. To this end, the evaluation database (which can beunderstood as a knowledge database of an expert system) is thenprocessed by inference rules of a rule set, which may, for example,encode logical conclusions, statistical derivations, and/or theknowledge from guidelines, as for example those issued by societies likethe American Heart Association. Inference rules may, for example,comprise logical and/or statistical operations and/or basic if-then-elserules. It is also conceivable that they are at least partially based onother knowledge-based methods (e.g. machine learning-basedclassification).

The derived knowledge, that is result evaluation data, is added back tothe evaluation database. All data sets remain linked by the respectiveinference rules, which in particular, as further discussed below, allowsan inference mechanism to be implemented, which keeps first and secondevaluation data (“atomic information”) and result evaluation data(“derived information”) consistent at any time.

In the next main step of the method, the results are presented to a userin an interactive presentation. The interactive presentation isconstructed to be able to present all information in the evaluationdatabase. Preferably, on initiation, the interactive presentation showsat least a part of the evaluation information as one of the majorevaluation purposes. In this manner, the essential information for alldetected medical features and anatomy is provided at once, for examplein an overview. By interacting with the displayed interactivepresentation using user interaction commands, the user can change theinteractive presentation to purposefully view evaluation data ofinterest, in particular also evaluation data from which evaluationinformation was derived in the rule-based diagnostic workup.

In this manner, automated results are combined with a user interactioncomponent which make the evaluation system's results fully traceable andcomprehensible. Consistency of the evaluation results with userexpectations is improved by deriving the local low-level information(second evaluation data) from deep learning-enabled second evaluationalgorithms and inferring high level information using inference rulescapturing domain knowledge. This combination allows to achieve the highaccuracy of deep learning algorithms, while maintaining thecomprehensibility and traceability of results.

The inventive approach may be employed in a large number of contexts.For example, in a computer-aided detection approach (CADe), exampleembodiments may be used to aid in localizing/marking regions that mayreveal specific abnormalities. In a computer-aided diagnosis (CADx)usage scenario, the CADe scenario is extended bycharacterizing/assessing disease, disease type, severity, stage andprogression. Different variations are conceivable. In a fullimplementation, even evaluation information-based patient managementrecommendations could be provided by example embodiments.

The method and/or evaluation system may be integrated into a readingworkstation, for example in advanced visualization workstations forinteractive reading of image data sets. In this context, the interactivereading may be expedited by providing the automatically determinedevaluation information and further evaluation data, which can be takeninto account by the user when assessing the image data sets, for exampleto provide a diagnosis.

In other embodiments, the method and/or evaluation system may also beintegrated into an imaging device, for example a computed tomographyscanner, providing further information already at the location of thescan. For example, basic, understandable and traceable assessments maybe provided alongside an image data set.

Further, the method and/or evaluation system may be linked with advanceddecision support approaches and/or may be part of a superordinateevaluation system applicable to many medical fields and/or examinationpurposes.

Preferably, as already laid out above, the second evaluation data setmay comprise a location of the medical feature and additional meta datadescribing the feature, in particular a severity in the case of apathology feature, in particular a lesion. Other meta data may compriseshape, composition and the like.

In preferred embodiments, the interactive presentation may compriseimage data from the image data set and/or visualization data derivedfrom at least one of the at least one image data set and/or from atleast one evaluation data set and/or at least one overview image. Inparticular, at least a part of the information from the evaluation datasets included in the interactive presentation may be presented byannotating and/or overlaying and/or modifying presented image dataand/or visualization data. Preferably, in particular regarding theinitial display of evaluation information, an overview image of at leastthe anatomical structure may be generated, preferably based on at leastone of the at least image data set. For example, the representativeoverview images may be generated providing the (essential) evaluationinformation for all detected anatomy and pathologies at once.Alternatively or preferably additionally, detailed images comprisingimage data and/or visualization data may be generated for single medicalfeatures and/or groups of medical features. For example, a detailedimage may be generated, preferably from image data, for each medicalfeature detected by the at least one second evaluation algorithm. Thesedetailed images may, in particular, be used to display first and/orsecond evaluation data. However, it is also possible to use detailedimages to present intermediate evaluation data, for example resultevaluation data used as input evaluation data to derive evaluationinformation. In especially preferred embodiments, such detailed imagesare displayed together with result evaluation data inferred by at leastone inference rule from first and/or second evaluation data, such thatthe inferred measurements may be verified against the displayedinformation of the detailed image by a user. In this manner, forexample, plausibility can be assessed.

In an especially advantageous embodiment, at least one evaluation dataset, in particular a first and/or second evaluation data set, ismodified based on received user input, wherein all evaluation data setslinked to the modified evaluation data set in the evaluation databaseare marked and/or all result evaluation data sets derived using themodified evaluation data set are updated using the respective inferencerules and/or at least a part of at least one evaluation data set fromwhich a modified evaluation data was derived is marked as less reliableand/or excluded from the interactive presentation. In a case in whichthe user is not satisfied with “atomic” or “derived” measurements, thatis evaluation data in the evaluation database, they can manually adaptthe evaluation data of at least one evaluation data set, in particularalso by interacting with the interactive presentation, for exampleaccording to a user modification command. If, for example, a lesion hasbeen classified as severe automatically, but the user comes to adifferent conclusion in his own assessment, they may accordingly modifythis information of the evaluation data set. The same is true, forexample, if a result seems implausible to the user. User input may bereceived and used to modify the respective evaluation data set. In otherwords, the evaluation database is updated. In preferred embodiments,this may lead to the inference rules automatically updating the resultevaluation data set derived from the modified evaluation data set by aforward chaining mechanism. At the same time, any information thatcontributed to the modified evaluation data set can be marked as lessreliable, in particular as outdated and/or invalid. In particular,further display of such less reliable information may be prevented. Insome examples, even evaluation data that contributed to the modifiedevaluation data set can be corrected, if possible. In this manner, atany time, always up-to-date and correct evaluation data is shown andimplausible situations are avoided. If other evaluation data sets areupdated and/or marked as outdated/invalid, this may also be displayed inthe interactive presentation, such that the user intuitively noticeswhich shown information was influenced by his modification.

In concrete embodiments, the anatomical structure may be hierarchicallydivided into segments in multiple hierarchy levels, wherein at least apart of the result data sets is determined for a defined hierarchy leveland/or comprises evaluation data relating to a defined segment. In thismanner, starting from localized, single medical features in the secondevaluation data sets, the evaluation data may be hierarchicallyorganized, for example by combining evaluation data relating to medicalfeatures in single segments to new, derived evaluation data sets. Inparticular, second evaluation data sets may already form lowesthierarchy level datasets and/or the rules set may comprise inferencerules for deriving lowest hierarchy level result evaluation data setsfrom at least one of the at least one second evaluation data set,respectively, and/or inference rules deriving a higher hierarchy levelresult evaluation data set from at least one lower, in particularadjacent hierarchy level input evaluation data set. In this manner,information may be propagated and evolved through multiple hierarchylevels, preferably finally resulting in anatomicalstructure-level/patient-level evaluation information. For example, atleast one inference rule may combine all lower-hierarchy and/or localevaluation data for a defined segment.

Statistical combination, aggregation, pooling and the like may all beused. If, for example, the medical features are lesions, the number oflesions for a segment and/or the largest volume of a lesion for asegment may be derived by an inference rule. In another example,segment-specific scores may be calculated.

In a concrete embodiment, the anatomical structure may be a blood vesseltree, in particular a coronary artery tree. For such blood vessel trees,for example, two hierarchy levels (which come in addition to the medicalfeature level) can be provided, namely blood vessel segments and thewhole blood vessel tree. Often, guidelines exist on how blood vesseltrees may be divided into segments and how these segments should belabeled.

It is noted that such hierarchically describable anatomical structuresare a particularly advantageous field to employ example embodiments. Insuch anatomical structures, often a large number of single medicalfeatures are taken into account to provide a general assessment for thewhole anatomical structure or parts thereof. By combining informationregarding single medical features in a controlled, rule-basedenvironment to create an evaluation database, in which the basic firstand second evaluation data set(s) are still retained, an expert systemproviding in-depth insight and understanding to a user is provided.

Preferably, additional patient data and/or statistical data may bereceived, wherein information from the additional patient data and/orstatistical data is used by at least one inference rule. That is, therule-based diagnostic workup of example embodiments may also joininformation about the patient and/or other external information toderive result evaluation information. For example, the evaluation systemmay be connected to at least one further, external information system,for example an electronic health record database, a radiologyinformation system, a hospital information system, and/or a picturearchiving and communication system (PACS). In this manner, all availableinformation can be taken into account to provide high-quality evaluationresults. Statistical information from an external source may, forexample, also be used in an inference rule, for example to determine apercentile based on patient gender, age, mass and the like. In otherwords, the patient may be characterized in relation to a referencepopulation.

It should be noted that the at least one image data set used as an inputis not necessarily restricted to a single examination. In embodiments,the input data sets may comprise information derived from multipleexaminations, which need not even share a common examination purpose.For example, image data sets from a time series of scans may be providedto the first and/or second evaluation algorithms. In embodiments, it mayeven be possible to use all image data sets of an electronic healthrecord of a patient to derive evaluation information regarding multipledifferent anatomical structures and/or medical conditions.

In preferred embodiments, if a finalizing user command is received, acombination data set comprising the at least one image data set and/orat least a part of the evaluation data of the evaluation data sets, inparticular at least the evaluation information, may be compiled andprovided for storing. That is, once the user has reviewed, optionallymodified and/or supplemented, the knowledge of the evaluation data sets,the evaluation results may be persisted and stored. While, inembodiments, the whole evaluation database may be stored as thecombination data set, it is also possible to extract essentialinformation and store it for later reference. For example, thecombination data set may be distributed to at least one other system ina structured and/or pictorial format. In preferred embodiments, thecombination data set may be provided in the DICOM format and/or forstorage in a picture archiving and communication system (PACS). Othersystems to which the combination data set may be forwarded are, forexample, reporting and/or decision support systems.

It should be noted at this point that it is, in principle, alsoconceivable that, instead of display of the interactive presentation,the combination data set may be directly derived from the evaluationdatabase and stored. For example, the review of the evaluation resultsmay be manually disabled. In such a case, the method performs the wholeuse-case fully automatically, including the final archiving, for examplein a PACS. The approval state of the combination data allows a finalrecipient of the evaluation results to distinguish between manuallyverified and fully automatically distributed evaluation results. Inother words, in embodiments, the finalizing user command can beunderstood as expressing approval with the evaluation results in theevaluation database.

In embodiments, the method may further comprise a preparatory step, inwhich, before applying any first or second evaluation algorithm, the atleast one image data set is analyzed regarding suitability for the firstand/or second evaluation algorithms and/or to determine suitable firstand/or second evaluation algorithms and/or, if multiple image data setsare received, associate image data sets to sets of first and/or secondevaluations algorithms. In the preparatory step, image data may bemediated. Each time at least one new image data set, for example in anew DICOM study, is received, the evaluation system automaticallyverifies if the at least one image data set contains at least one imagedata set concerning the anatomical structure and/or medical condition,in other words is useful for the evaluation purpose of the concreteembodiment. If no relevant image data set is received, furtherprocessing is stopped. If multiple evaluation purposes may be served,received image data sets may be parsed and categorized according to theevaluation purposes. If multiple candidate image data sets are availablefor a given evaluation purpose, they may be prioritized according tospecific prioritization rules. Finally, for each examination purpose,automatic processing may be triggered on at least one best suitableimage data set. If example embodiments are implemented in a suitableenvironment, also optimized image data sets may be requested, forexample, from an imaging device. In such a preparatory step, of course,meta data provided with the image data, for example in a DICOM study,may of course also be taken into account.

In concrete embodiments, for each set of first and second evaluationalgorithms serving an evaluation purpose, requirement informationcomprising requirements regarding the quality and/or the content ofimage data to be evaluated is provided or determined. Each time at leastone image data set of a patient is received for evaluation regarding acertain evaluation purpose, each image data set may be analyzed todetermine a suitability information regarding each associated evaluationpurpose, the suitability information indicating at least fulfilment ornon-fulfilment of the requirements of the corresponding requirementinformation. Only at least one image data set whose suitabilityinformation indicates fulfilment of the requirement of the requirementinformation of at least one corresponding evaluation purpose, isforwarded for evaluation to the corresponding evaluation algorithms ofthe evaluation purpose and/or at least one user information and/or imageprocessing action is executed for at least one evaluation purpose whoseassociated suitability information all indicate non-fulfilment of therequirements of the requirement information.

It is noted that, in the preparatory step, also other preparatorymeasures regarding the image data sets may be executed, for exampleapplying preparatory image processing, defining and/or deriving imagedata sets to be actually evaluated and the like.

In a general remark, if multiple image data sets are evaluated, if theyare not yet registered and/or motion-corrected, this might be also bedone in a/the preparatory step. For example, in some applications, someimage data sets may be better suited for segmentation and/or labellingof the anatomy, while other image data sets may be more suitable forderiving second evaluation data sets. If the respective image data setsare registered, however, location information can be transferred betweenthem. This is also true for different image data sets evaluated by theat least one second evaluation algorithm.

In another general remark, the inference rules may not be limited toclassical rule operations, but the rule set may also employ moreelaborate classification and/or machine learning approaches which mayalso involve image-based classification. The results of suchclassifications are fed back to the inference mechanism in the same wayas if they had been computed using regular rule-based processing.

Generally, example embodiments may also be used as a secondary orconcurrent reader. In such a usage scenario, the evaluation systemgenerates its output in parallel to the initial reading of the case by aradiologist. In this manner, the (human) reader can be supported byavoiding any false negative findings.

Example embodiments will now be further explained with respect to anespecially advantageous embodiment, namely the use case of a cardiacstudy. In this advantageous embodiment, the anatomical structure is acoronary artery tree, wherein at least one coronary computed tomographyangiography scan and at least one calcium scoring computed tomographyscan are received as input data sets and the evaluation informationcomprises at least one atherosclerotic disease-related score and atleast one calcium score for the patient. In particular, two evaluationpurposes, namely atherosclerotic disease and calcium scoring, areinvolved, wherein the individual method steps for both evaluationprocesses will be mostly discussed separately in the following.

In this exemplary, advantageous embodiment, an evaluation system isprovided allowing the fully automated evaluation of cardiac computedtomography scans, covering both coronary computed tomography angiographyscans (CCTA scans) and calcium scoring scans (CaSc scans). This enablesa more comprehensive assessment of the status of disease than with knownproducts or algorithms. In particular, a refined categorization ofseverity of coronary artery disease is enabled, like, for example,proposed in an article by M. B. Mortenson et al, “CAD Severity onCardiac CTA Identifies Patients With Most Benefit of Treating LDLCholesterol to ACC/AHA and ESC/EAS Targets”, JACC CardioVasc Imaging2020, 51936-878X(20)30322-3.

Further, in this particular field of application, example embodimentsmay be used to rule out coronary artery disease, since healthy patientsare effectively identified. For these patients, the time to a clinicalreport can potentially be reduced significantly as the end user mustonly confirm that no findings have been identified. Furthermore, adesignated list of clinicians may be notified if cases with severe formsof disease which potentially require immediate further management, forexample if a total occlusion of one of the coronary arteries is found.Cases with potentially more significant disease can be prioritizedhigher in the reading queue, ensuring a timely evaluation. In thismanner, complication rates may be reduced.

Providing a combined system evaluating both CCTA and calcium scoringscans may be advantageous on its own. That is, a computer-implementedmethod for evaluating image data sets, which have been acquired in atleast one coronary computed tomography angiography scan and at least onecalcium scoring computed tomography scan, of an imaging region of apatient, is conceivable, wherein at least one evaluation informationdescribing at least one medical condition in an anatomical structure,which is the coronary artery tree, of the imaging region and comprisingat least one atherosclerotic disease-related score and at least onecalcium score for the patient is determined, wherein the methodcomprises segmenting and labelling the anatomical structure using atleast one first evaluation algorithm to generate at least one firstevaluation data set and entering the at least one first evaluation dataset into an evaluation database, determining at least one secondevaluation data set describing at least one local medical feature in theanatomical structure using at least one second evaluation algorithm andentering the at least one second evaluation data set into the evaluationdatabase, determining the at least one evaluation information byapplying inference rules of a rule set, wherein each inference rulederives at least one result evaluation data set from at least one, inparticular at least two, input evaluation data set and wherein theevaluation database is augmented by adding the result evaluation dataset for each rule application linked to the respective at least oneinput evaluation data set in the evaluation database, using theevaluation database to output an interactive presentation comprisinginformation from at least a part of the evaluation data set of theevaluation database and/or to create a combination data set comprisingthe image data set and/or at least a part of the evaluation data of theevaluation data sets, in particular at least the evaluation information.

In a preferred embodiment, as a prepossessing step, the cardiac-gatedcoronary computed tomography angiography scan is split into severalimage data sets according to multiple phases of the heart cycle, whereinthe image data set of a predefined heart phase is selected forevaluation and/or at least one of the image data sets and/or a subset ofat least one image data set best meeting requirements of at least one ofthe first and/or second evaluation algorithm is forwarded to therespective first and/or second evaluation algorithm. Optionally, whenreceiving a new DICOM study, it may be automatically verified if theexamination contains at least one cardiac-gated coronary computedtomography angiography scan of the coronary arteries. If not, furtherprocessing may be stopped. If a cardiac-gated coronary computedtomography angiography scan is received, the scan may be split into theavailable heart phases, that is, intervals of the full heart cycle, forexample 0 to 10%, 10 to 20%, and so on. Image data sets may be compiledfor each heart phase, however, in preferred embodiments, only a bestsuitable image data set is used for further processing regarding theatherosclerotic disease. A suitability measure can be derived from thegrade of fulfilment of requirements of a corresponding requirementinformation. Preferably, at least one requirement relates to coronarymotion, such that the best suitable image data set would be the oneshowing the least coronary motion. While it is, in principle,conceivable to choose the best suitable heart phase and hence the bestsuitable image data set according to experience, for example as theheart phase closest to 70% (diastolic heart phase), it is preferred toanalyze the image data sets, in particular to derive at least onequality parameter related to coronary motion. The best suitable imagedata set may then be chosen based on this at least one qualityparameter. In embodiments, the image suitability classification may alsobe performed on a per-vessel or a per-segment basis in order to selectthe best suitable heart phase for assessing different parts of thecorona arteries separately. In other words, after the at least one firstevaluation data set has been determined, which describes segments of thecoronary artery tree, for at least one segment and/or at least one groupof segments, the determination of meeting the requirements is performedon subsets only showing the segment and/or group, respectively. Theseembodiments allow selecting the optimal image data for furtherevaluation and hence improve the quality of the evaluation results.

In a concrete embodiment, to segment and label the coronary artery treeas an anatomical structure:

centerlines of coronary arteries may be detected by at least one of theat least one first evaluation algorithm,

the coronary lumen surrounding the centerlines may be detected by atleast one of the at least one first evaluation algorithm, and

the detected coronary arteries may be classified according to apredefined and/or user-selectable classification scheme of the coronaryartery tree into segments for labelling by at least one of the at leastone first evaluation algorithm,

such that the at least one first evaluation data set describes, for eachpoint in the coronary artery tree, to which segment the point belongs,the local course of the segment and the local shape of the segment.

The primary goal of the coronary computed tomography angiographyevaluation is to evaluate the presence of atherosclerotic disease in thecoronary arteries. In a preferred embodiment, thus, anatomy modelling ofthe coronary arteries may be performed in three steps. In a first step,the centerlines of the coronary arteries may be detected byautomatically tracing the coronary arteries in the CCTA image data set.The resulting coronary artery centerlines represent the morphology andtopology of the patient-specific coronaries in a tree model. Forconcrete implementation of this first step, centerline tracingalgorithms already known in the state of the art may also be employedhere. As an example, it is referred to U.S. Pat. No. 10,210,612 B2. Itis noted that, besides tracing of the contrast-enhanced coronaryarteries, this exemplary approach also supports tracing across stents,chronic total occlusions, and coronary artery bypass grafts.

In a second step for anatomy modelling of the coronary arteries, thecoronary lumen is detected. Based on the centerline tree and the CCTAimage data, the coronary lumen may be detected, again using lumensegmentation algorithms known in the state of the art. Exemplarily, itis referred to US Patent Application US 2019/0130578 A1.

In a third step, to enable the evaluation and presentation of resultsusing proper medical terminology, the detected coronary arteries areclassified according to a standardized labelling scheme for the coronaryartery segments, comprising, for example, segments like “proximal LAD(Left Anterior Descending)”, “mid RCA (Right Coronary Artery)” or“Obtuse Marginal 1”. For example, the labelling scheme proposed by theSSCT and American Heart Association in J. Leipsic et al., “SCCTguidelines for the interpretation and reporting of coronary CTangiography: a report of the Society of Cardiovascular ComputedTomography Guidelines Committee”, J. Cardiovasc. Comput. Tomogr. 8(2014), Pages 342-358, can be employed. Labelling algorithms alreadyproposed in the state of the art may be used. As an example, it isreferred to the article by A. Fisher et al., “Deep Learning BasedAutomated Coronary Labeling For Structured Reporting Of Coronary CTAngiography In Accordance With SCCT Guidelines”, Journal ofCardiovascular Computed Tomography 14.3 (2020), pages 21-22, or to PaulKlein et al, “Method for Automated Coronary Tree Labeling UsingBidirectional Tree structured Recurrent Neural Networks”, Vol. 99,published 27 Jun. 2019. In general, labelling algorithms are of ageneric nature and applicable to other labelling schemes as well.

As a result of these three processing steps for segmenting andlabelling, it is known for each point in the coronary arteries to whichcoronary artery segment it belongs, the shape of the coronary arterylumen, the course of the segments and, if the mentioned class ofcenterline tracing algorithms is used, also the course of coronaryartery bypass grafts, if applicable.

Preferably, when determining second evaluation data relating toatherosclerotic disease, the at least one second evaluation algorithm,in particular based on the centerlines, detects and analyzes lesion inthe coronary artery tree such that a second evaluation data set isgenerated for each lesion, comprising at least one information chosenfrom the group comprising

a start position and end position of the lesion, in particular along thecenterline,

a plaque class, in particular chosen from calcified plaque,non-calcified plaque and mixed plaque,

a plaque vulnerability information derived from or describing thepresence of at least one vulnerability indicator, in particular chosenfrom low attenuation values, in particular attenuation values lower than30 HU, positive remodeling, spotty calcification, and napkin ring signs,

a stenting information describing the presence of a stent from anearlier intervention.

Preferably based on the coronary centerlines, the coronary lumen and theCCTA image data set, a lesion detection algorithm may detect anydiseased part of the coronary arteries as at least one of the least onesecond evaluation algorithm. A lesion (as medical feature) may representthe full spectrum of disease, starting from very small focal lesions torather long lesions capturing diffuse disease. During the lesiondetection process, preferably, the following information may beautomatically determined for each lesion:

The start and stop positions representing positions on the coronarycenterlines at the last and first healthy positions before and after thelesion. The entire path along the coronary centerlines between thesepositions is considered as diseased.Plaque composition: the lesion detection algorithm may classify theplaque composition of the lesion into calcified, non-calcified and mixedplaque.Plaque vulnerability: the lesion detection algorithm may identify thepresence of one of the following features, which may indicate apotential vulnerability of the lesion: low attenuation values, positiveremodeling, spotty calcification, and napkin ring signs. A lesion may beattributed with multiple of these vulnerability indicators at the sametime. For example, positive remodeling is an outward compensatoryremodeling in which the arterial wall grows outward in an attempt tomaintain a constant lumen diameter, while negative remodeling is definedas a local shrinkage of vessel size. The presence of a ring of highattenuation around certain coronary artery plaques is usually callednapkin ring sign. The lesion detection algorithm may further classifystents from prior interventions as such.

Example embodiments are not limited to a specific type of lesiondetection algorithm. Lesion detection algorithms known from the state ofthe art, in particular able to determine the above-mentionedinformation, may be used. As an example, it is referred to U.S. Pat. No.9,881,372 B2. If all the information described above are determined, asa result of the at least one lesion detection algorithm, it is known foreach point in the coronary arteries if it is part of a coronary lesion(or a stent, if applicable). For each lesion, the plaque composition andthe presence of vulnerable plaque indicators is known.

In preferred embodiments, diameter values of the coronary arteries foreach lesion along the centerline are determined, in particular by atleast one second evaluation algorithm and/or at least one inference rulecombining at least one first evaluation data set and at least one secondevaluation data set. In particular if the coronary lumen has beensegmented, diameter information may be computed for each position alongthe coronary centerline of a lesion. Hence, the diameters at the startand stop positions of the lesion as well as the position of minimalluminal narrowing may be determined. This diameter information is alsoadded to the evaluation database.

In an especially advantageous embodiment, the inference rules may deriveresult evaluation data from the first and/or second evaluation data setsfor three hierarchy levels, namely a lesion level (medical featurelevel) relating to single lesions, a segment level relating to singlesegments of coronary arteries, and a patient level relating to the wholecoronary artery tree. As already discussed for the general case, such ahierarchical structure further improves understandability for the user.In a concrete embodiment, regarding lesions detected by the at least onesecond evaluation algorithm, by the inference rules,

on the lesion level, result evaluation data chosen from the groupcomprising a segment the lesion belongs to, a quantitative stenosisgrade, a classification of the plaque as vulnerable or non-vulnerableand a classification as an ostium lesion or a bifurcation lesion,on the segment level, result evaluation data chosen from the groupcomprising a maximum degree of obstruction, a number of lesions and/or alesion plaque composition, andon the patient level, result evaluation data, in particular theevaluation information, chosen from the group comprising aclassification of the atherosclerotic disease as a one-, two-, orthree-vessel disease with or without involvement of the left mainsegment, at least one classification grade, in particular a CAD-RADSgrade, and at least one image-based score,may be determined.

Image-based scores may, for example, comprise a segment involvementscore, a segment stenosis score, a Duke index, and/or a CT Leaman score.

Preferably, at least a part of the inference rules may follow thedefinitions of the SCCT Guideline and/or the CAD-RADS ReportingGuideline. For the first guideline, please see the already-referencedarticle by J. Leipsic et al., for the second guideline, please refer tothe article by R. C. Curry et al., “Coronary Artery Disease—Reportingand Data System (CAD-RADS): An Expert Consensus Document of SCCT, ACRand NASCI: Endorsed by the ACC.”, in JACC Cardiovasc Imaging 9 (2016),pages 1099-1113.

In preferred embodiments, the inference rules classify the evaluationdata sets in three hierarchy levels of abstraction. On a lesion level,for each lesion, the following information may be determined. First ofall, the lesion location may define the at least one coronary arterysegment that is affected by the lesion. Furthermore, the quantitativestenosis grade, for example “narrowing of 53%” may be computed. This maybe done, for example, by applying a traditional qualitative comparativeanalysis (QCA) approach, using the diameter information mentioned abovederived at the start/stop position of the lesion as well as the minimumdiameter within the lesion. Other interpolation schemes for estimatingthe healthy reference diameter are conceivable. In another approach,image-based classification may be applied, for example based on deeplearning algorithms. The quantitative stenosis grade may be used toclassify the lesion's severity, for example into “none”, “minimal”,“mild”, “moderate”, “severe”, and “total occlusion”. Furthermore, theabove-mentioned plaque class (plaque composition information) and theabove-mentioned plaque vulnerability information indicating presence ofvulnerability indicators may be used to classify the plaque as“vulnerable” or “non-vulnerable”. Finally, the position of the lesion inthe coronary artery tree may be used to classify it as “bifurcationlesion” or “ostial lesion”, if applicable.

Regarding the segment level, the inference rules propagate theinformation available on lesion level to the coronary segments. For eachsegment, for example, its maximum degree of obstruction, the number oflesions as well as their plaque composition may be provided.

Regarding the patient level, the classifications of the individualsegments on the segment level may be used to compute differentassessments of the overall state of disease in the coronary artery tree.In particular, the disease may be classified as one-/two-/three-vesseldisease with or without involvement of the left main segment.Additionally or alternatively, a classification following the CAD-RADsreporting standard may be provided for the given case. For example, agrade “CAD-RADs 3/v/s” describes a maximum degree of obstruction of 50to 69% with vulnerable plaques and presence of a stent. “CAD-RADs 0”means that no disease has been detected. Additionally, image-basedscores, for example segment involvement score, segment stenosis score,Duke index and CT Leaman score may be computed and displayed.

All inferred information as well as their dependencies are, asdescribed, added to the evaluation database as result evaluation dataset or sets, such that at any time the evaluation database can beupdated consistently, if required.

It is noted that the degree of luminal narrowing is just one of therelevant aspects for the assessment of the risk of future cardiovascularevents. Studies have shown that the type and amount of plaques in thevessels wall help to further discriminate the risk. Especially theso-called low-attenuation plaques seem to be highly correlated withmajor adverse cardiac vents (MACV). Hence, plaque quantification methodsmay be included regarding the at least one second evaluation algorithmand/or the inference rules. In this manner, a comprehensive assessmentof coronary artery disease may be provided.

In concrete embodiments, the interactive presentation may comprise, inparticular as visualization data, a coronary unfolded view, inparticular as overview image, and/or a schematic view, in particular asoverview image, and/or at least one lesion specific view, in particularbased on a curved planar reformation (CPR).

In particular, overview images may be generated for lesion level,segment level and patient level, each documenting the evaluationresults. For example, the following images may be provided.

All detected coronary artery may be visualized in a single overviewimage together with the detected lesions and their grade as a coronaryunfolded view. Graphical information may be displayed to show whichparts of the coronary arteries have been detected as diseased. As anexample for such an overview image, it is referred to not yet laid openEuropean Patent application EP 19 212 538.3.

Additionally or alternatively, at least one schematic image representinga generic model of the coronary artery tree may be generated, in whichthe degree of obstruction of the coronary artery segment may bedisplayed as a color-coded image.

Furthermore, lesion-specific views may be generated as detailed imagesfor each detected coronary lesion. Such a lesion-specific view may, forexample, show a curved planar reformation of the effected vesseltogether with the lumen segmentation created on that coronary artery. Inaddition, cross-sectional views may be displayed for the lesion startand/or stop position as well as the detected position having maximalobstruction. Based on the contours the user can assess visually if thederived stenosis grading information is plausible/reasonable.

In concrete embodiments, the interactive presentation, in particular theoverview and/or detailed images and/or information, may be displayed ina user interface, for example using a web-based application where theuser can manually adjust and/or modify evaluation results in case he isnot satisfied with first and/or second evaluation data and/or resultevaluation data. On the lesion level, for example, he may modify thestenosis grade from “moderate” to “severe”. As a result, a forwardpropagation of the modification through the evaluation database results.On lesion level, information on lesion level may be marked as notreliable/invalid. However, in embodiments, it is also possible to modifyinformation from which a modified result evaluation data set is derived.In the mentioned example, the stenosis percentage may be updated to 70%,as this is the lower threshold value for a lesion to be classified assevere. The segment level and patient level classifications are updatedaccordingly. For example, a CAD-RADs grade may change from 3 to 4a.Similarly, a backtracking is performed to identify which informationcontributed to the modified information in the modified evaluation dataset. In the given example, the stenosis grade is derived from the lumensegmentation. The modification of the user implies that the lumensegmentation was not optimal. Hence, it is hidden from thelesion-specific view. In this manner, no wrong information is displayedat any time. The display interactive presentation is always keptconsistent automatically by keeping the evaluation database consistent.

Regarding automatic calcium scoring, when receiving, for example, a newDICOM study, it may be automatically verified if the examinationcontains at least one cardiac-gated non-contrast calcium scoringreconstruction. If not, further processing may be stopped. In the casethat multiple image data sets are available, they may be prioritizedbased on reconstruction kernel and/or date/time information. Finally,the best suitable at least one image data set is forwarded to thesubsequent processing steps.

Regarding automatic anatomy detection and labelling, the same way ofproceeding as regarding the atherosclerotic disease evaluation may beused. In some embodiments, however, other segmentation schemes may beemployed. Regarding the second evaluation algorithms, at least oneautomatic calcium scoring algorithm may be applied to the respectivecalcium scoring image data set. Such an automatic calcium scoringalgorithm may automatically recognize single coronary calcifications andmay label them according to the segment in which the calcification hasbeen detected. Regarding an example of such an automatic calcium scoringalgorithm, it is referred to the article by M. Sandstedt et al.,“Evaluation of an AI-based, automatic coronary artery calcium scoringsoftware”, in Eur Radiol 30(2020), pages 1671-1678.

Each detected single coronary calcification together with its meta data,in particular comprising segment labels, may be added to the evaluationdatabase as second evaluation data sets. As regarding the CCTAevaluation, the inference rules for calcium scoring may also deriveresult evaluation data for the three hierarchy levels named below, thatis the lesion level relating to single lesions, the segment levelrelating to single segments of coronary arteries, and the patient levelrelating to the whole coronary artery tree. Regarding the calciumscoring, the lesion level may also be called calcification level. In aconcrete embodiment, by the inference rules,

on the calcification level, result evaluation data comprising a segmentthe calcification belongs to,on the segment level, result evaluation data chosen from the groupcomprising a calcification score, a number of calcifications and/or avolume of the calcifications, and on the patient level, resultevaluation data, in particular the evaluation information, chosen fromthe group comprising a respectively the calcification score, a number ofcalcifications, a volume of the calcifications, a percentile of thepatient, and an arterial age of the patient, may be determined. Forexample, the definitions in the already mentioned article by C. H.McCollough et al. may be followed.

Regarding calcification scores, the total and segment-specific Agatstonscore may be determined. Furthermore, the total and segment-specificvolume of the calcifications may be provided. The total number ofcalcifications may be provided on the segment level as well as on thepatient level. Furthermore, a patient percentile may be computed tochoosing a reference population, for example based on patientinformation like gender, age and the like. For reference populations,for example, the MESA database may be used (see A. L. McClelland et al.,“Distribution of coronary artery calcium by race, gender, and age:results from the Multi-Ethnic Study of Atherosclerosis (MESA)”,Circulation 113(2006), Seiten 30-37.

Finally, based on a patient's gender, age, ethnicity and/or otherpatient information and a reference population, the arterial age may becomputed. An arterial age computation was, for example, published in anarticle by R. L. McClelland et al., “Arterial Age as a Function ofCoronary Artery Calcium (from the Multi-Ethnic Study of Atherosclerosis[MESA])”, Am J Cardiol. 2009 January 1; 103(1), pages 59-63.

Also in this case, all inferred evaluation results are, of course, addedto the evaluation database including their dependencies/linkage byinference rules.

Regarding the interactive presentation of the evaluation results, forcalcium scoring, overview images may again be generated for lesion level(calcification level), segment level and patient level, which documentthe segmentation and calcification results. In a concrete embodiment, acalcium overview image may be provided which visualizes all detectedcalcifications in maximum intensity projections through the imagingregion in a color-coded way. In this manner, from a single image, it canbe seen which calcifications have been detected and if there arepotentially missed lesions. The calcium overview image also allows toget a high-level understanding if the calcifications are attributed tothe correct vessel/segment. Additionally or alternatively, the originalcomputed tomography slices may be provided with the labelledcalcifications as overlay to enable a detailed evaluation of thecalcification labelling at, for example, the left main bifurcation.Finally, a percentile chart relating to at least one calcium score maybe included in the interactive presentation. Such a percentile chart mayvisualize the patient-specific calcification score, for example Agatstonscore, in the context of the selected reference population. Regardingcalcium scoring, it is noted that only few modifying operations need tobe supported in the interactive presentation. For example, the user maymanually change the patient information and/or a selected referencedatabase. Any manual change of these values triggers an update of therisk categorization, in particular the percentile information and/or thearterial age.

In further embodiments, the at least one image data set may comprisefunctional image data of the coronary artery tree, wherein, asevaluation data, at least one hemodynamic parameter, in particular afractional flow reserve (FFR), is determined. Hence, example embodimentsmay also provide functional assessment of coronary artery disease. Inparticular regarding lesions of intermediate severity, a functionalassessment is recommended by guidelines. While hemodynamic measurementshave traditionally been performed by actually measuring inside the bloodvessels, in particular measuring the pressure drop across a lesion(fractional flow reserve—FFR), it has recently been proposed to alsodetermine hemodynamic parameters non-invasively based on imagingtechniques. For example, FFR values may be determined from computedtomography image data sets based on simulation and/or machine learning.Additionally or alternatively, other approaches for hemodynamicassessment, like coronary volume-to-mass-ratio and/or lesion-subtendedmyocardial mass can also be employed.

In an extension, in addition to evaluating the calcification in thecoronary artery tree, evaluation data regarding calcification in theheart valves and/or the aortic root may be determined by at least onesecond evaluation algorithm and/or inference rule. In this manner,calcifications in the aortic root and/or heart valves can also bequantified. According to guidelines, a CCTA and/or CaSc report shouldalso include a statement regarding the presence of calcifications in theaortic root and the heart valves. If present, the amount ofcalcification should be graded into mild/moderate/severe. Hence,advantageously, the method and systems of example embodiments can beextended to also provide support for detection and/or grading ofcalcifications in the aortic root and heart valves.

Furthermore, in preferred embodiments, at least one heart parameter maybe determined as additional evaluation data by at least one secondevaluation algorithm and/or inference rule, in particular at least oneheart chamber volume and/or at least one heart muscle size. For example,based on contrasted and/or non-contrasted cardiac computed tomographyimage data sets, segmentation of the ventricles and the atrium is alsopossible. Based on such segmentation results, a quantification of theindividual chamber size as well as the sizing of the heart muscle ispossible. If image data sets have been acquired at more than one pointin time, additionally, the left and right ventricular function may bequantified. Quantification algorithms regarding heart parameters havealready been proposed in the state of the art and may also be applied inexample embodiments.

Of course, the general concept of providing an explainable artificialintelligence evaluation system aggregating localized anatomical and/orpathological findings and measurements via inference rules to moreabstract region- or patient-wise assessments, as described by exampleembodiments, can also be applied to other radiological examinations. Forexample, a method according to at least one example embodiment may beused for evaluating image data sets of a liver examination and/or adigital breast tomosynthesis examination.

An evaluation system according at least one example embodiment forevaluating at least one image data set of an imaging region of a patientto determine at least one evaluation information describing at least onemedical condition in an anatomical structure of the imaging regioncomprises:

an image interface for receiving the at least one image data set,a storage for storing an evaluation database and a rule set,at least one first determination unit for segmenting and labelling theanatomical structure using at least one first evaluation algorithm togenerate at least one first evaluation data set and determining at leastone second evaluation data set describing at least one local medicalfeature in the anatomical structure using at least one second evaluationalgorithm, wherein the at least one first determination unit is adaptedto enter the at least one first and the at least one second evaluationdata set into the evaluation database,a second determination unit for determining the at least one evaluationinformation by applying inference rules of the rule set, wherein eachinference rule derives at least one result evaluation data set from atleast one, in particular at least two, input evaluation data set,wherein the second determination unit is adapted to augment theevaluation database by adding the result evaluation data sets for eachrule application linked to the respective input evaluation data sets inthe evaluation database,a user interface unit for outputting an interactive presentationcomprising information from at least a part of the evaluation data setsof the evaluation database, wherein the initially displayed interactivepresentation comprises at least one of the at least one evaluationinformation and the user interface is adapted to receive a userinteraction command related to at least one chosen result evaluationdata set and to update the interactive presentation to includeinformation of at least one input evaluation data set from which thechosen result evaluation data set is derived.

All features and remarks relating to the evaluation method of exampleembodiments accordingly apply to the evaluation system according toexample embodiments, such that the same advantages can be accomplished.In particular, the evaluation system may be configured to perform amethod according to example embodiments. The evaluation system maycomprise at least one computing device, in particular comprising the atleast one storage and/or at least one processor. The functional unitsmay, in particular, be implemented at least partially in hardware and/orat least partially in software. Further functional units regardingembodiments of the method may, of course, be added. Preferably, theevaluation system may also comprise an output interface, in particularfor outputting the combination data set, in particular the wholeevaluation database, for example to a picture archiving andcommunication system (PACS) and/or an information system and/or anelectronic health record. In particular, such an output interface mayalso serve as an input interface, in particular the image interface, ifimage data sets and/or input data comprising the image data sets, forexample a DICOM study, are retrieved from the PACS and/or theinformation system and/or the electronic health record. As alreadyexplained, the evaluation system may be a stand-alone system, but mayalso be integrated into other systems and devices, for example in animaging device or, preferably, in a reading work station.

A computer program according to at least one example embodiment can bedirectly loaded into a storage device of a computing device of anevaluation system and comprise program such that, if the computerprogram is executed on the computing device of the evaluation system,the steps of a method according to the at least one example embodimentare performed. The computer program may be stored on an electronicallyreadable storage medium according to at least one example embodiment,which thus comprises control information comprising at least onecomputer program according to the at least one example embodiment, suchthat, if the electronically readable storage medium is used in acomputing device of an evaluation system, a method according to the atleast one example embodiment is performed. The electronically readablestorage medium according to at least one example embodiment maypreferably be a non-transitory storage medium, for example a CD-ROM.

FIG. 1 is a flow chart showing high-level steps of a method according toat least one example embodiment. In a step S1, which is an optionalpreparatory step, input data comprising at least one image data set isreceived. For example, input data may be provided as a DICOM file of acertain examination (often also called a DICOM study). The input data isautomatically passed and categorized according to the evaluationpurpose. If no image data sets regarding the evaluation purposesprovided by the concrete embodiment are found, processing is stopped. Ifmultiple image data sets relating to a served evaluation purpose areavailable in the input data, they may be prioritized according tospecific prioritization rules. In particular, for each evaluationpurpose served, the ensuing automated processing is triggered using theat least one best suitable image data set (for example highestpriority). In the preparatory step S1, furthermore, image data sets canbe pre-processed, for example regarding the distribution of image data,performance and the like. For example, in a motion-triggeredacquisition, image data sets may be generated for different motionphases, and the best suitable image data set may be chosen.

In a step S2, automatic anatomy detection and labelling takes placeusing at least one first evaluation algorithm. The at least one firstevaluation algorithm preferably comprises at least one deeplearning-enabled automatic segmentation algorithm to extract therelevant parts of anatomy, in particular the anatomical structure ofinterest for the evaluation purpose. Geometrical models may be generatedand automatically labelled using a labelling scheme. For example, theanatomical structure may be divided into segments each being named usinga proper medical terminology. The labelling scheme may follow a definedhierarchical structure comprising at least two hierarchy levels, in thesimplest case the whole anatomical structure as the highest hierarchylevel and segments as the lowest hierarchy level. However, more than twohierarchy levels are conceivable, for example regarding a blood vesseltree, where the blood vessel tree can be divided into different bloodvessels (first segments), which again may be divided into blood vesselsegments (second, lower-level segments). It is noted that the localmedical features detected in step S3 provide a further, additional lowhierarchy level in this context. As a result of step S2, each point inthe anatomical structure can be associated with a segment, whosedesignation is known.

In step S3, medical features are automatically detected and associatedmeta data is determined using at least one second evaluation algorithm.Preferably, the at least one second evaluation algorithm comprises atleast one deep learning-enabled detection algorithm. That is,preferably, both the at least one first evaluation algorithm and the atleast one second evaluation algorithm, which use at least one of the atleast one image data set as input data, comprise at least one trainedevaluation function. The automatic detection process provides thelocation of the medical feature, for example a lesion and/or pathology,as well as meta data associated with the medical feature, for example acharacterization of the medical feature or information relating todisease severity. The availability of specific meta data attributesdepends on the actual models used during the detection procedure.

The results of the steps S2 and S3 are first evaluation data and secondevaluation data, respectively, as first and second evaluation data sets.These first and second evaluation data sets are entered into anevaluation database.

In a step S4, the rule-based diagnostic workup using a rule setcomprising at least one inference rule is performed, such that theevaluation database, in which all knowledge regarding the current caseis gathered, can be augmented by further, result evaluation data sets.The evaluation database is processed by the inference rules, which maycomprise logical operations, statistical operations and/orif-then-else-rules and other linking operations. In particular, furtherpatient data and/or statistical data can also be evaluated by at least apart of the inference rules, for example patient gender, patient height,patent size, patient age, patient ethnicity and the like. Statisticaldata may preferably relate to reference populations to determineevaluation data, in particular evaluation information, like percentilesand/or anatomical age. While the rules may, in particular, at leastpartially concern physical and/or technical correlations, they may atleast partially also encode knowledge from societal guidelines, forexample definitions of certain disease scores and/or other clinicallyrelevant evaluations results. Each inference rule uses at least oneinput evaluation data set from the evaluation database and results in atleast one result evaluation data set. The derived knowledge of theresult evaluation data set is added back to the evaluation database. Ofcourse, also the inference rules linking the evaluation data sets arestored in the evaluation database, such that the information in thedatabase can be kept consistent at any time. It is noted that thisrule-based diagnostic workup may also join information derived frommultiple image data sets and even multiple evaluation purposes, inparticular, as already described, with other information sources, like,for example, from external information systems.

It should be noted at this point that the (minimal), designatedevaluation purpose is the determination of at least one evaluationinformation. This evaluation information, which may be a disease score,a total quantification of a certain substance or the like, is comprisedby the evaluation data of the evaluation database, in particular as somefinal result evaluation data in at least one result evaluation data set,for example at the highest hierarchy level. However, the methoddescribed here derives and stores additional evaluation data to create asort of expert system being able to explain how the evaluationinformation was derived and, in particular present underlyinginformation or even image data. In other words, by using the evaluationdatabase and all evaluation data contained in it, the user canunderstand how the method came to its conclusions based on the providedimage data and, in particular also allows the user to influence theautomatically prepared evaluation results, in particular the evaluationinformation, in an efficient way, as will be further described below.

In a step S5, an interactive presentation is generated and provided at auser interface. The interactive presentation serves to present theevaluation results. The interactive presentation may comprise image datafrom the at least one image data set, visualization data derived from atleast one of the at least one image data set and/or from at least oneevaluation data set, and/or especially generated overview images. Forexample, image data or visualization data may be annotated, overlayedand/or modified, for example color-coded, to include information fromthe evaluation database in the corresponding images. Preferably, theinteractive presentation may be oriented at the hierarchy levelsdiscussed above, for example summarizing information on the medicalfeature level, at least one low hierarchy level, for example a segmentlevel, and at least one high hierarchy level, for example anatomicalstructure level or patient level. As the evaluation information, in manycases, relates to the highest hierarchy level, for example the patientlevel, an initially displayed interactive presentation may mainly focuson this highest hierarchy level and also show at least a part of theevaluation information. The user may then interact with the interactivepresentation using user interaction commands such that the interactivepresentation may be updated to include further evaluation data from theevaluation database, in particular information underlying an informationitem, in particular evaluation data set, the user interaction wasrelated to.

Regarding the medical feature level, for example lesion level, detailedimages may be generated for each detected medical feature, which may inparticular also show information from the first and second evaluationdata sets. By verifying the inferred evaluation data against furtherdisplayed information, in particular the detailed image, a user can, atany time, assess the plausibility of the evaluation result.

If the user does not agree with one or more evaluation results, he canmanually modify evaluation data in the evaluation database. Hence, in astep S6, it is checked whether user input regarding a modification isreceived, in which case the respective at least one evaluation data setis accordingly modified and the evaluation database is updated in workupstep S4, also leading to an update of the interactive presentation instep S5. As the evaluation data sets are all linked by the inferencerules, by using a forward chaining mechanism, all evaluation dataeffected by the modification can be updated. In some cases, it may alsobe possible to update evaluation data from which the modified evaluationdata set was derived. For example, if a classification is changed, avalue underlying this classification may be moved into an intervalcorresponding to this classification. Alternatively or additionally,information from which the modified evaluation data set was derived mayalso be marked as less reliable, in particular invalid and/or outdated.All evaluation data linked to modified evaluation data may also bemarked in the interactive presentation, such that the user canimmediately see which information was updated due to his modification.

If, however, in step S6, the user approves the contents of theevaluation database, in particular the evaluation information, in a stepS7, the evaluation results may be persisted and/or distributed to othersystems, in particular in structured and/or pictorial format, forexample based on the DICOM standard. In other words, a combination dataset may be compiled from the evaluation database including all relevantimage data and/or evaluation data. For example, a DICOM study initiallyreceived in step S1 may be supplemented with further information fromthe evaluation data sets and archived in a PACS and/or electronic healthrecord. Of course, it is also possible to provide evaluation results viaan output interface to a reporting system and/or a decision supportsystem.

A concrete embodiment enabling the combined evaluation of a coronarycomputed tomography angiography scan and a calcium scoring scan will nowbe described, in particular regarding the interactive presentation. Sucha cardiac study, which may be supplied as a DICOM study, as alreadydiscussed, refers to the coronary artery tree as an anatomical structureand usually, as the evaluation purpose, has the goal to determine atleast one atherosclerotic disease score and at least one calcificationscore as evaluation information. According to known labelling schemesalready discussed in the general part of the description, the coronaryartery tree may be divided into a plurality of segments, such thatevaluation results will be generated and presented on a medical featurelevel (lesion level/calcification level), a segment level and a patientlevel/anatomical structure level.

Regarding step S1, if a new DICOM study of the heart of a patient isreceived, the examination may, in preferred embodiments, be split intoimage data sets relating to different phases of the heart cycle. This ispossible because usually, CCTA scans are cardiac-gated. From these imagedata sets relating to different heart phases, the best suitable can bechosen, which, in this case, may be the image data set showing the leastcoronary motion. Of course, also other quality parameters may be derivedto determine a suitability measure, such that the image data set havingthe highest suitability measure can be chosen for further processingregarding the CCTA evaluation. If multiple image data sets suitable forcalcium scoring evaluation are received, also, quality parametersrelating to requirements for evaluation may be determined and the bestsuitable image data set may be selected. For example, quality parametersin the case of the calcium scoring evaluation may be related to the usedreconstruction kernel.

Regarding step S2, for the CCTA evaluation, three substeps may be used,namely detection of the coronary artery centerlines by a centerlinetracing algorithm, detection of the coronary artery lumen by a lumensegmentation algorithm and classifying and labelling the detectedcoronary arteries according to a labelling scheme using a labellingalgorithm. As a result of these three substeps, from the resulting firstevaluation data set, it is known for each point in the coronary arteriesto which coronary segment they belong, which the shape of the coronaryartery lumen is and how the local course of the segments is. If coronaryartery bypass grafts are also detected by one of these first evaluationalgorithms, their course also becomes known, if applicable.

Regarding automatic calcium scoring, these results may also be used.

In step S3, for the CCTA evaluation, at least one lesion detectionalgorithm is used as the at least one second evaluation algorithm.Preferably, the resulting second evaluation data set for each lesioncomprise the lesion start and stop position along the centerlines, theplaque composition (calcified, non-calcified, or mixed), a plaquevulnerability information describing the presence of plaquevulnerability indicators like low attenuation, positive remodeling,spotty calcification and/or napkin ring signs, and whether the lesion isclassified as a stent from a prior intervention.

Regarding the calcium scoring evaluation, single calcifications aredetected, located, and quantified and the respective second evaluationdata sets are stored in the evaluation database.

In step S4, regarding the CCTA evaluation, diameter information may bederived from the lumen information in the at least one first evaluationdata set for each lesion. Further inference rules may follow thedefinitions of the SCCT guideline and/or the CAD-RADS reportingguideline already cited above. For example, a rule may comprise checkingwhether an obstruction percentage is in a certain interval and/orwhether the lumen diameter exceeds a certain threshold. Other rules mayadditively and/or multiplicatively combine evaluation data values, or,generally, comprise calculation formulas.

In this preferred embodiment, the inference rules classify the detectedinformation in three hierarchy levels of abstraction, namely the lesionlevel, the segment level and the patient level. Regarding the lesionlevel, the following evaluation data is provided: lesion location (whichat least one coronary artery segment the lesion affects), quantitativestenosis grade, a classification of the plaque as vulnerable ornon-vulnerable, and a classification as ostium lesion or bifurcationlesion, if applicable. On the segment level, a maximum degree ofobstruction, a number of lesions and/or a lesion plaque composition maybe comprised by result evaluation data. Finally, on the patient level,result evaluation data derived by the inference rules may comprise aclassification of the atherosclerotic disease as a one-, two-, orthree-vessel disease with or without involvement of the left mainsegment, at least one classification grade, and at least one image-basedscore. These derived evaluation data may all be evaluation informationrequested as evaluation purpose. For example, a classification followingthe CAD-RADs reporting standard may be provided, together withimage-based scores like segment involvement score, segment stenosisscore, duke index, and CT Leaman score.

Regarding the automatic calcium scoring evaluation, the Agatston scoremay be provided on patient level and segment level, as well as a volumescore describing the total and segment-specific volume of thecalcifications. Further, on segment level and patient level, the totalnumber of calcifications may be provided. Finally, on patient level, apercentile regarding a reference population and/or an arterial age maybe determined and provided.

FIGS. 2 to 5 illustrate the interactive presentation of results in anembodiment, wherein the results of the CCTA evaluation and the calciumscoring evaluation are presented in one and the same user interface 1.FIG. 2 shows an initial view provided after evaluation in steps S1 to S4is completed. A first tab 2 a of the user interface 1 is active, suchthat an overview over the CCTA evaluation results is displayed. Thefurther tabs 2 b, 2 c and 2 d relate to segment level informationregarding CCTA, lesion level information regarding the CCTA, and calciumscoring evaluation information, respectively.

On the left side in FIG. 2, an overview image 3, in this case a coronaryunfolded view generated from the at least one CCTA image data set, isdisplayed, showing the anatomical structure 4, in this case the coronaryartery tree, with annotations 5 and overlays 6 relating to at least apart of the detected lesions. As can be seen, lesions are marked bypartially enframing the respective coronary artery sections, for examplein yellow, while textual information can be added as annotations 5.Using buttons 7, another overview image, in particular a schematicaloverview image, can be alternatively selected.

On the right side of user interface 1, information from the evaluationdata relating to the patient level is shown, in particular theevaluation information 8 and further evaluation data 9. The patientlevel evaluation results shown here may comprise the CAD-RADs grade, thedisease type and/or risks scores. At least for a part of the displayedinformation from the evaluation database, the user may interact tomodify evaluation data and/or receive additional information, inparticular in a view as further discussed below.

FIG. 3 shows the segment level tab 2 b, in this case comprising aschematic overview image 10, in which the segments of the coronaryartery tree are color-coded according to the severity of their lesions.The segment level evaluation results are further displayed as text in atable 11 on the right side of the user interface 1.

FIG. 4 shows the user interface of the interactive presentation when theCCTA lesion level tab 2 c is active. On the left side, a detailed image12 which may be composed of several sub-images regarding a certainlesion is shown. This lesion is indicated by text 13, while the lesionscan be passed using buttons 14. Of course, if a user interaction commandregarding one of the lesions for segments shown in the previous statesof the interactive presentation is received, the corresponding lesion isdirectly selected and shown. In the detailed image 12, the startposition, the stop position and the position of maximum narrowing may bedocumented together with the lumen segmentation. First and secondevaluation data as well as result evaluation data relating to the lesionare displayed in area 15.

Finally, FIG. 5 shows the user interface 1 when the calcium scoring tab2 d is active. In this case, on the left side, an overview image 16visualizes the detected calcifications according to the segment theyhave been attributed to. On the right side evaluation data 17, 18regarding the segment level and the patient level, respectively, isdisplayed. The information from the patient level, evaluation data 17,also comprises the evaluation information regarding calcium scoring.

FIG. 6 is a schematic drawing of an evaluation system 19 according to atleast one example embodiment. The evaluation system 19 comprises atleast one computing device 20 having a storage device 21 and at leastone processor. In the storage device 21, the rule set 22 and theevaluation database 23 may be stored.

The evaluation system 29 comprises a first input and output interface24, by which the evaluation system 19 is connected to at least oneimaging device 25, at least one information system 26 and at least onePACS 27. Image data sets, in particular in a DICOM study, may bereceived via the interface 24 from the imaging device 25, theinformation system 26 and/or the PACS 27. In the information system 26,for example, electronic health records 28 of patients may be stored.Furthermore, the at least one information system 26 may providestatistical data regarding refence populations.

If a DICOM study is received via the interface 24, it is prepared forevaluation in an optional preparation unit 29 implementing step S1. Afirst determination unit 30 applies the first and second evaluationalgorithms according to steps S2 and S3 of FIG. 1, and stores theresulting first and second evaluation data sets into a study-specific oreven evaluation purpose-specific evaluation database 23. In a seconddetermination unit 31, the inference rules of the rule set 22 areapplied according to step S4, augmenting the evaluation database 23. Itis noted that additional information can be taken into account by thesecond determination unit 31, for example from patient data and/orstatistical data also received via the interface 24, for example fromthe information system 26. In a user interface unit 32, the interactivepresentation is generated and managed, in particular also regarding userinput and user interaction commands. Via a second input/output interface33, a display device 34 and an input device 35 may be controlled anduser input as well as user commands may be received. If a user-initiatedmodification of at least one evaluation data set in the evaluationdatabase 23 is performed, the second determination unit 31 again appliesthe inference rules of the rule set 22 to update all evaluation datasets effected by the modification.

If a user approval input is received in the user interface unit 32, thecombination data set may be compiled according to step S7 anddistributed via interface 24 for storing or further processing/viewing.

In a preferred embodiment, the evaluation system 19 may be integratedinto a reading work station, which then also comprises the displaydevice 34 and the input device 35. In other embodiments, the evaluationsystem 19 may be provided as a stand-alone system, integrated into animaging device 25, or coupled to a reporting system and/or decisionsupport system.

The drawings are to be regarded as being schematic representations andelements illustrated in the drawings are not necessarily shown to scale.Rather, the various elements are represented such that their functionand general purpose become apparent to a person skilled in the art. Anyconnection or coupling between functional blocks, devices, components,or other physical or functional units shown in the drawings or describedherein may also be implemented by an indirect connection or coupling. Acoupling between components may also be established over a wirelessconnection. Functional blocks may be implemented in hardware, firmware,software, or a combination thereof.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions,layers, and/or sections, these elements, components, regions, layers,and/or sections, should not be limited by these terms. These terms areonly used to distinguish one element from another. For example, a firstelement could be termed a second element, and, similarly, a secondelement could be termed a first element, without departing from thescope of example embodiments. As used herein, the term “and/or,”includes any and all combinations of one or more of the associatedlisted items. The phrase “at least one of” has the same meaning as“and/or”.

Spatially relative terms, such as “beneath,” “below,” “lower,” “under,”“above,” “upper,” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. It will beunderstood that the spatially relative terms are intended to encompassdifferent orientations of the device in use or operation in addition tothe orientation depicted in the figures. For example, if the device inthe figures is turned over, elements described as “below,” “beneath,” or“under,” other elements or features would then be oriented “above” theother elements or features. Thus, the example terms “below” and “under”may encompass both an orientation of above and below. The device may beotherwise oriented (rotated 90 degrees or at other orientations) and thespatially relative descriptors used herein interpreted accordingly. Inaddition, when an element is referred to as being “between” twoelements, the element may be the only element between the two elements,or one or more other intervening elements may be present.

Spatial and functional relationships between elements (for example,between modules) are described using various terms, including “on,”“connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitlydescribed as being “direct,” when a relationship between first andsecond elements is described in the disclosure, that relationshipencompasses a direct relationship where no other intervening elementsare present between the first and second elements, and also an indirectrelationship where one or more intervening elements are present (eitherspatially or functionally) between the first and second elements. Incontrast, when an element is referred to as being “directly” on,connected, engaged, interfaced, or coupled to another element, there areno intervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between,” versus “directly between,” “adjacent,” versus“directly adjacent,” etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the singular forms “a,” “an,” and “the,”are intended to include the plural forms as well, unless the contextclearly indicates otherwise. As used herein, the terms “and/or” and “atleast one of” include any and all combinations of one or more of theassociated listed items. It will be further understood that the terms“comprises,” “comprising,” “includes,” and/or “including,” when usedherein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof. As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items. Expressions such as “at least one of,” whenpreceding a list of elements, modify the entire list of elements and donot modify the individual elements of the list. Also, the term “example”is intended to refer to an example or illustration.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which example embodiments belong. Itwill be further understood that terms, e.g., those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

It is noted that some example embodiments may be described withreference to acts and symbolic representations of operations (e.g., inthe form of flow charts, flow diagrams, data flow diagrams, structurediagrams, block diagrams, etc.) that may be implemented in conjunctionwith units and/or devices discussed above. Although discussed in aparticularly manner, a function or operation specified in a specificblock may be performed differently from the flow specified in aflowchart, flow diagram, etc. For example, functions or operationsillustrated as being performed serially in two consecutive blocks mayactually be performed simultaneously, or in some cases be performed inreverse order. Although the flowcharts describe the operations assequential processes, many of the operations may be performed inparallel, concurrently or simultaneously. In addition, the order ofoperations may be re-arranged. The processes may be terminated whentheir operations are completed, but may also have additional steps notincluded in the figure. The processes may correspond to methods,functions, procedures, subroutines, subprograms, etc.

Specific structural and functional details disclosed herein are merelyrepresentative for purposes of describing example embodiments. Thepresent invention may, however, be embodied in many alternate forms andshould not be construed as limited to only the embodiments set forthherein.

Units and/or devices according to one or more example embodiments may beimplemented using hardware, software, and/or a combination thereof. Forexample, hardware devices may be implemented using processing circuitrysuch as, but not limited to, a processor, Central Processing Unit (CPU),a controller, an arithmetic logic unit (ALU), a digital signalprocessor, a microcomputer, a field programmable gate array (FPGA), aSystem-on-Chip (SoC), a programmable logic unit, a microprocessor, orany other device capable of responding to and executing instructions ina defined manner. Portions of the example embodiments and correspondingdetailed description may be presented in terms of software, oralgorithms and symbolic representations of operation on data bits withina computer memory. These descriptions and representations are the onesby which those of ordinary skill in the art effectively convey thesubstance of their work to others of ordinary skill in the art. Analgorithm, as the term is used here, and as it is used generally, isconceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of optical, electrical, or magnetic signals capable of beingstored, transferred, combined, compared, and otherwise manipulated. Ithas proven convenient at times, principally for reasons of common usage,to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like.

It should be borne in mind that all of these and similar terms are to beassociated with the appropriate physical quantities and are merelyconvenient labels applied to these quantities. Unless specificallystated otherwise, or as is apparent from the discussion, terms such as“processing” or “computing” or “calculating” or “determining” of“displaying” or the like, refer to the action and processes of acomputer system, or similar electronic computing device/hardware, thatmanipulates and transforms data represented as physical, electronicquantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

In this application, including the definitions below, the term ‘module’or the term ‘controller’ may be replaced with the term ‘circuit.’ Theterm ‘module’ may refer to, be part of, or include processor hardware(shared, dedicated, or group) that executes code and memory hardware(shared, dedicated, or group) that stores code executed by the processorhardware.

The module may include one or more interface circuits. In some examples,the interface circuits may include wired or wireless interfaces that areconnected to a local area network (LAN), the Internet, a wide areanetwork (WAN), or combinations thereof. The functionality of any givenmodule of the present disclosure may be distributed among multiplemodules that are connected via interface circuits. For example, multiplemodules may allow load balancing. In a further example, a server (alsoknown as remote, or cloud) module may accomplish some functionality onbehalf of a client module.

Software may include a computer program, program code, instructions, orsome combination thereof, for independently or collectively instructingor configuring a hardware device to operate as desired. The computerprogram and/or program code may include program or computer-readableinstructions, software components, software modules, data files, datastructures, and/or the like, capable of being implemented by one or morehardware devices, such as one or more of the hardware devices mentionedabove. Examples of program code include both machine code produced by acompiler and higher level program code that is executed using aninterpreter.

For example, when a hardware device is a computer processing device(e.g., a processor, Central Processing Unit (CPU), a controller, anarithmetic logic unit (ALU), a digital signal processor, amicrocomputer, a microprocessor, etc.), the computer processing devicemay be configured to carry out program code by performing arithmetical,logical, and input/output operations, according to the program code.Once the program code is loaded into a computer processing device, thecomputer processing device may be programmed to perform the programcode, thereby transforming the computer processing device into a specialpurpose computer processing device. In a more specific example, when theprogram code is loaded into a processor, the processor becomesprogrammed to perform the program code and operations correspondingthereto, thereby transforming the processor into a special purposeprocessor.

Software and/or data may be embodied permanently or temporarily in anytype of machine, component, physical or virtual equipment, or computerstorage medium or device, capable of providing instructions or data to,or being interpreted by, a hardware device. The software also may bedistributed over network coupled computer systems so that the softwareis stored and executed in a distributed fashion. In particular, forexample, software and data may be stored by one or more computerreadable recording mediums, including the tangible or non-transitorycomputer-readable storage media discussed herein.

Even further, any of the disclosed methods may be embodied in the formof a program or software. The program or software may be stored on anon-transitory computer readable medium and is adapted to perform anyone of the aforementioned methods when run on a computer device (adevice including a processor). Thus, the non-transitory, tangiblecomputer readable medium, is adapted to store information and is adaptedto interact with a data processing facility or computer device toexecute the program of any of the above mentioned embodiments and/or toperform the method of any of the above mentioned embodiments.

Example embodiments may be described with reference to acts and symbolicrepresentations of operations (e.g., in the form of flow charts, flowdiagrams, data flow diagrams, structure diagrams, block diagrams, etc.)that may be implemented in conjunction with units and/or devicesdiscussed in more detail below. Although discussed in a particularlymanner, a function or operation specified in a specific block may beperformed differently from the flow specified in a flowchart, flowdiagram, etc. For example, functions or operations illustrated as beingperformed serially in two consecutive blocks may actually be performedsimultaneously, or in some cases be performed in reverse order.

According to one or more example embodiments, computer processingdevices may be described as including various functional units thatperform various operations and/or functions to increase the clarity ofthe description. However, computer processing devices are not intendedto be limited to these functional units. For example, in one or moreexample embodiments, the various operations and/or functions of thefunctional units may be performed by other ones of the functional units.Further, the computer processing devices may perform the operationsand/or functions of the various functional units without subdividing theoperations and/or functions of the computer processing units into thesevarious functional units.

Units and/or devices according to one or more example embodiments mayalso include one or more storage devices. The one or more storagedevices may be tangible or non-transitory computer-readable storagemedia, such as random access memory (RAM), read only memory (ROM), apermanent mass storage device (such as a disk drive), solid state (e.g.,NAND flash) device, and/or any other like data storage mechanism capableof storing and recording data. The one or more storage devices may beconfigured to store computer programs, program code, instructions, orsome combination thereof, for one or more operating systems and/or forimplementing the example embodiments described herein. The computerprograms, program code, instructions, or some combination thereof, mayalso be loaded from a separate computer readable storage medium into theone or more storage devices and/or one or more computer processingdevices using a drive mechanism. Such separate computer readable storagemedium may include a Universal Serial Bus (USB) flash drive, a memorystick, a Blu-ray/DVD/CD-ROM drive, a memory card, and/or other likecomputer readable storage media. The computer programs, program code,instructions, or some combination thereof, may be loaded into the one ormore storage devices and/or the one or more computer processing devicesfrom a remote data storage device via a network interface, rather thanvia a local computer readable storage medium. Additionally, the computerprograms, program code, instructions, or some combination thereof, maybe loaded into the one or more storage devices and/or the one or moreprocessors from a remote computing system that is configured to transferand/or distribute the computer programs, program code, instructions, orsome combination thereof, over a network. The remote computing systemmay transfer and/or distribute the computer programs, program code,instructions, or some combination thereof, via a wired interface, an airinterface, and/or any other like medium.

The one or more hardware devices, the one or more storage devices,and/or the computer programs, program code, instructions, or somecombination thereof, may be specially designed and constructed for thepurposes of the example embodiments, or they may be known devices thatare altered and/or modified for the purposes of example embodiments.

A hardware device, such as a computer processing device, may run anoperating system (OS) and one or more software applications that run onthe OS. The computer processing device also may access, store,manipulate, process, and create data in response to execution of thesoftware. For simplicity, one or more example embodiments may beexemplified as a computer processing device or processor; however, oneskilled in the art will appreciate that a hardware device may includemultiple processing elements or processors and multiple types ofprocessing elements or processors. For example, a hardware device mayinclude multiple processors or a processor and a controller. Inaddition, other processing configurations are possible, such as parallelprocessors.

The computer programs include processor-executable instructions that arestored on at least one non-transitory computer-readable medium (memory).The computer programs may also include or rely on stored data. Thecomputer programs may encompass a basic input/output system (BIOS) thatinteracts with hardware of the special purpose computer, device driversthat interact with particular devices of the special purpose computer,one or more operating systems, user applications, background services,background applications, etc. As such, the one or more processors may beconfigured to execute the processor executable instructions.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language) or XML (extensible markuplanguage), (ii) assembly code, (iii) object code generated from sourcecode by a compiler, (iv) source code for execution by an interpreter,(v) source code for compilation and execution by a just-in-timecompiler, etc. As examples only, source code may be written using syntaxfrom languages including C, C++, C#, Objective-C, Haskell, Go, SQL, R,Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5,Ada, ASP (active server pages), PHP, Scala, Eiffel, Smalltalk, Erlang,Ruby, Flash®, Visual Basic®, Lua, and Python®.

Further, at least one example embodiment relates to the non-transitorycomputer-readable storage medium including electronically readablecontrol information (processor executable instructions) stored thereon,configured in such that when the storage medium is used in a controllerof a device, at least one embodiment of the method may be carried out.

The computer readable medium or storage medium may be a built-in mediuminstalled inside a computer device main body or a removable mediumarranged so that it can be separated from the computer device main body.The term computer-readable medium, as used herein, does not encompasstransitory electrical or electromagnetic signals propagating through amedium (such as on a carrier wave); the term computer-readable medium istherefore considered tangible and non-transitory. Non-limiting examplesof the non-transitory computer-readable medium include, but are notlimited to, rewriteable non-volatile memory devices (including, forexample flash memory devices, erasable programmable read-only memorydevices, or a mask read-only memory devices); volatile memory devices(including, for example static random access memory devices or a dynamicrandom access memory devices); magnetic storage media (including, forexample an analog or digital magnetic tape or a hard disk drive); andoptical storage media (including, for example a CD, a DVD, or a Blu-rayDisc). Examples of the media with a built-in rewriteable non-volatilememory, include but are not limited to memory cards; and media with abuilt-in ROM, including but not limited to ROM cassettes; etc.Furthermore, various information regarding stored images, for example,property information, may be stored in any other form, or it may beprovided in other ways.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. Shared processor hardware encompasses asingle microprocessor that executes some or all code from multiplemodules. Group processor hardware encompasses a microprocessor that, incombination with additional microprocessors, executes some or all codefrom one or more modules. References to multiple microprocessorsencompass multiple microprocessors on discrete dies, multiplemicroprocessors on a single die, multiple cores of a singlemicroprocessor, multiple threads of a single microprocessor, or acombination of the above.

Shared memory hardware encompasses a single memory device that storessome or all code from multiple modules. Group memory hardwareencompasses a memory device that, in combination with other memorydevices, stores some or all code from one or more modules.

The term memory hardware is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium is therefore considered tangible and non-transitory. Non-limitingexamples of the non-transitory computer-readable medium include, but arenot limited to, rewriteable non-volatile memory devices (including, forexample flash memory devices, erasable programmable read-only memorydevices, or a mask read-only memory devices); volatile memory devices(including, for example static random access memory devices or a dynamicrandom access memory devices); magnetic storage media (including, forexample an analog or digital magnetic tape or a hard disk drive); andoptical storage media (including, for example a CD, a DVD, or a Blu-rayDisc). Examples of the media with a built-in rewriteable non-volatilememory, include but are not limited to memory cards; and media with abuilt-in ROM, including but not limited to ROM cassettes; etc.Furthermore, various information regarding stored images, for example,property information, may be stored in any other form, or it may beprovided in other ways.

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks andflowchart elements described above serve as software specifications,which can be translated into the computer programs by the routine workof a skilled technician or programmer.

Although described with reference to specific examples and drawings,modifications, additions and substitutions of example embodiments may bevariously made according to the description by those of ordinary skillin the art. For example, the described techniques may be performed in anorder different with that of the methods described, and/or componentssuch as the described system, architecture, devices, circuit, and thelike, may be connected or combined to be different from theabove-described methods, or results may be appropriately achieved byother components or equivalents.

Further, as noted similarly above, the use of the undefined article “a”or “one” does not exclude that the referred features can also be presentseveral times. Likewise, the term “unit” or “device” does not excludethat it includes several components, which may also be spatiallydistributed.

Although at least some example embodiments have been described indetail, example embodiments are not limited by the disclosed examplesfrom which the skilled person is able to derive other variations withoutdeparting from the scope of example embodiments.

1. Computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined, wherein the method comprises: segmenting and labelling the anatomical structure using at least one first evaluation algorithm to generate at least one first evaluation data set and entering the at least one first evaluation data set into an evaluation database; determining at least one second evaluation data set describing at least one local medical feature in the anatomical structure using at least one second evaluation algorithm and entering the at least one second evaluation data set into the evaluation database; determining the at least one evaluation information by applying inference rules of a rule set, wherein each inference rule derives at least one result evaluation data set from at least one input evaluation data sets and wherein the evaluation database is augmented by adding the result evaluation data sets for each rule application linked to the respective input evaluation data sets in the evaluation database; and outputting an interactive presentation comprising information from at least a part of the evaluation data sets of the evaluation database, wherein the initially displayed interactive presentation comprises at least one of the at least one evaluation information and, when a user interaction command related to at least one chosen result evaluation data set is received, updating the interactive presentation to include information of at least one input evaluation data set from which the chosen result evaluation data set is derived.
 2. The method according to claim 1, wherein the interactive presentation comprises (i) at least one of image data from the image data set, (ii) visualization data derived from at least one of the at least one image data set or from at least one evaluation data set, or (iii) at least one overview image.
 3. The method according to claim 2, wherein at least a part of the information from the evaluation data sets included in the interactive presentation is presented by at least one of annotating presented image data, overlaying the presented image data, modifying the presented image data or visualization data.
 4. The method according to claim 1, wherein at least one evaluation data set is modified based on received user input, wherein all evaluation data sets linked to the modified evaluation data sets in the evaluation database are at least one of (i) marked, (ii) all result evaluation data sets derived using the modified evaluation data sets are updated using the respective inference rules, (iii) at least a part of at least one evaluation data set or sets from which a modified evaluation data set was derived is marked as less reliable, or (iv) excluded from the interactive presentation.
 5. The method according to claim 1, wherein the anatomical structure is hierarchically divided into segments in multiple hierarchy levels, wherein at least a part of the result data sets (i) is determined for a defined hierarchy level or (ii) comprises evaluation data relating to a defined segment.
 6. The method according to claim 5, wherein the rules set comprises at least one of (i) inference rules for deriving lowest hierarchy level result evaluation data sets from at least one of the at least one second evaluation data sets, respectively, or (ii) inference rules deriving a higher hierarchy level result evaluation data set from at least one lower hierarchy level input evaluation data set.
 7. The method according to claim 1, wherein at least one of additional patient data or statistical data are received, wherein information from at least one of the additional patient data or statistical data is used by at least one inference rule.
 8. The method according to claim 1, wherein, if a finalizing user command is received, a combination data set comprising at least one of the at least one image data set or at least a part of the evaluation data of the evaluation data sets, is compiled and provided for storing.
 9. The method according to claim 1, further comprising: deciding, in which, before applying any first or second evaluation algorithm, the at least one image data set is analyzed regarding (i) suitability for at least one of the first evaluation algorithm or the second evaluation algorithm, (ii) to determine at least one of a suitable first evaluation algorithm or a suitable second evaluation algorithm, or (iii) if multiple image data sets are received, associate image data sets to sets of the at least one of the first evaluation algorithm or the second evaluation algorithm.
 10. The method according to claim 1, wherein the anatomical structure is a coronary artery tree, wherein at least one coronary computed tomography angiography scan and at least one calcium scoring computed tomography scan are received as image data sets and the evaluation information comprises at least one atherosclerotic disease-related score and at least one calcium score for the patient.
 11. The method according to claim 10, wherein, as a preprocessing step, the cardiac-gated coronary computed tomography angiography scan is split into several image data sets according to multiple phases of the heart cycle, wherein at least one of (i) the image data set of a predefined heart phase is selected for evaluation or (ii) at least one of the image data set or a subset of the at least one image data set best meeting requirements of at least one of the first evaluation algorithm or the second evaluation algorithm is forwarded to the respective at least one of the first evaluation algorithm or the second evaluation algorithm.
 12. The method according to claim 11, wherein, after the at least one first evaluation data set has been determined, which describes segments of the coronary artery tree, for at least one of at least one segment or at least one group of segments, the determination of meeting the requirements is performed on subsets only showing at least one of the segment or group, respectively.
 13. The method according to claim 10, wherein, to segment and label the coronary artery tree as anatomical structure: centerlines of coronary arteries are detected by at least one of the at least one first evaluation algorithm, the coronary lumen surrounding the centerlines is detected by at least one of the at least one first evaluation algorithm, and the detected coronary arteries are classified according to at least one of a predefined classification scheme or a user-selectable classification scheme of the coronary artery tree into segments for labelling by at least one of the at least one first evaluation algorithm, such that the at least one first evaluation data set describes, for each point in the coronary artery tree, to which segment the point belongs, the local course of the segment and the local shape of the segment.
 14. The method according to claim 10, wherein the at least one second evaluation algorithm detects and analyzes lesions in the coronary artery tree such that a second evaluation data set is generated for each lesion, comprising at least one information chosen from the group comprising a start position and end position of the lesion, a plaque class, a plaque vulnerability information derived from or describing the presence of at least one vulnerability indicator, positive remodeling, spotty calcification, and napkin ring signs, or a stenting information describing the presence of a stent from an earlier intervention.
 15. The method according to claim 10, wherein the inference rules derive result evaluation data from at least one of the first evaluation data set or the second evaluation data set for three hierarchy levels, the three hierarchy levels including a lesion level relating to single lesions, a segment level relating to single segments of coronary arteries, and a patient level relating to the whole coronary artery tree.
 16. The method according to claim 10, wherein the interactive presentation includes at least one of a coronary unfolded view, a schematic view, at least one lesion-specific view, or a percentile chart relating to at least one calcium score.
 17. An evaluation system for evaluating at least one image data set of an imaging region of a patient to determine at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region, wherein the evaluation system comprises: an image interface configured to receive the at least one image data set; a storage device configured to store an evaluation database and a rule set; at least one first determination unit configured to segment and label the anatomical structure using at least one first evaluation algorithm to generate at least one first evaluation data set and determine at least one second evaluation data set describing at least one local medical feature in the anatomical structure using at least one second evaluation algorithm, wherein the at least one first determination unit is adapted to enter the at least one first and the at least one second evaluation data set into the evaluation database; a second determination unit configured to determine the at least one evaluation information by applying inference rules of the rule set, wherein each inference rule derives at least one result evaluation data set from at least one input evaluation data set, wherein the second determination unit is adapted to augment the evaluation database by adding the result evaluation data sets for each rule application linked to the respective input evaluation data sets in the evaluation database; and a user interface unit configured to output an interactive presentation comprising information from at least a part of the evaluation data sets of the evaluation database, wherein the initially displayed interactive presentation comprises at least one of the at least one evaluation information and the user interface is adapted to receive a user interaction command related to at least one chosen result evaluation data set and to update the interactive presentation to include information of at least one input evaluation data set from which the chosen result evaluation data set is derived.
 18. A computer program, when executed by a computing device of an evaluation system, is configured to cause the evaluation system to perform the method according to claim
 1. 19. An electronically readable storage medium having instructions, when executed by a computing device of an evaluation system, is configured to cause the evaluation system to perform the method of claim
 1. 20. An evaluation system for evaluating at least one image data set of an imaging region of a patient to determine at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region, wherein the evaluation system comprises: a storage device configured to store an evaluation database and a rule set; and processing circuitry configured to execute computer-readable instructions to cause the evaluation system to, receive the at least one image data set, segment and label the anatomical structure using at least one first evaluation algorithm to generate at least one first evaluation data set and determine at least one second evaluation data set describing at least one local medical feature in the anatomical structure using at least one second evaluation algorithm, wherein the at least one first determination unit is adapted to enter the at least one first and the at least one second evaluation data set into the evaluation database, determine the at least one evaluation information by applying inference rules of the rule set, wherein each inference rule derives at least one result evaluation data set from at least one input evaluation data set, wherein the second determination unit is adapted to augment the evaluation database by adding the result evaluation data sets for each rule application linked to the respective input evaluation data sets in the evaluation database, and output an interactive presentation comprising information from at least a part of the evaluation data sets of the evaluation database, wherein the initially displayed interactive presentation comprises at least one of the at least one evaluation information and the user interface is adapted to receive a user interaction command related to at least one chosen result evaluation data set and to update the interactive presentation to include information of at least one input evaluation data set from which the chosen result evaluation data set is derived. 